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<?xml version="1.0" standalone="yes"?> <Paper uid="W98-1402"> <Title>2.* . COMMUNICATIVE GOAL-DRIVEN NL GENERATION AND DATA-DRIVEN GRAPHICS GENERATION: AN ARCHITECTURAL SYNTHESIS FOR MULTIMEDIA</Title> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> SCOTLAND, U.K. </SectionTitle> <Paragraph position="0"> j. a. bateman@s tir : ac. uk Thomas Kamps JSrg Kleinz Klaus Reichenberger</Paragraph> <Section position="1" start_page="0" end_page="0" type="sub_section"> <SectionTitle> Industrial Process and Integrated Publication and Information System Communications Systems Institute </SectionTitle> <Paragraph position="0"/> </Section> </Section> <Section position="3" start_page="0" end_page="0" type="metho"> <SectionTitle> Abstract * </SectionTitle> <Paragraph position="0"> In this paper we presen t a system for automatically producing multimedia pages of information that draws both from results in data-driven aggregation in information visualization and from results in communicative-goal oriented natural language generation. Our system constitutes an architectural synthesis of these two directions, allowing a beneficial cross-fertilization of research methods. We suggest that data-driven visualization provides a general approach to aggregation in NLG, and that text planning allows higher user-responsiveness in visualization via automatic diagram design.</Paragraph> <Paragraph position="1"> * 1 Introduction In this paper we present one of the most significant system-architectural *results relevant for NLG achieved within the KOMET-PAVE multimedia page generation experiment (GMD-IPSI: 1994-1996). l Based on previous, separate experiences in natural language generation (see: Teich & Bateman 1994, Bateman & Teich 1995) and in automatic diagram design and visualization (see: Htiser, Reichenberger, Rostek & Streitz 1995), the KOMET-PAVE experiment sought to combine NLG and visualization into a single integrated information presentation system capable * of producing effectively designed pages of information analogous to 'overviews' found in print-based publications such as encyclopediae or magazines. During this work, it became evident that there were significant overlaps both in the processes and organizations of data most supportive of information presentation. Moreover, the individual approaches offered complementary solutions for presentation subproblems that proved independent of the particular presentation modalities for which they were originally developed. A thorough architectural synthesis was therefore strongly indicated.</Paragraph> <Paragraph position="2"> The particular complementarity that provides the focus of the present paper is the following. First, it is widely accepted in both NLG and graphic design that the design decisions adopted must be sensitive not only to communicative purposes and the &quot;user' but also to contingent and emergent organizational properties of the data. However, the effectiveness of the solutions proposed for these is in complementary distribution across the two modalities. Approaches to respecting communicative purpose are underdeveloped in graphic design, while NLG has powerful techniques for imposing adherence to communicative purpose (e.g., goal-driven text planning); and, similarly, approaches to data-driven organization (i.e., 'aggregation') are comparatively weak in NLG, while automatic visualization now has a range of powerful techniques for identifying emergent organizational properties of large datasets. The architecture constructed in KOMET-PAVE builds on a combination of these individually developed techniques, resulting in a significant 'cross-fertilization' of approaches.</Paragraph> <Paragraph position="3"> * I KOMET ('Knowledge-oriented production of multimodal documents') and PAVE ('Publication and advanced visualization environments') were two departments of the German National Research Center for Information Technology's (GMD) institute for Integrated Publication and Information Systems (IPSI) in Darmstadt that cooperated closely for the work described in this paper. The authors would therefore like to thank all the members of those departments who contributed, and particularly Lothar Rostek, We organize the discussion as follows. We first introduce the visualization and automatic diagram design methods developed within the PAVE component of our system, drawing explicit attention to the similarities between the decisions made during diagram generation and those necessary during NL generation (Section 2). ThisProvides necessary background to our claim that the methods and algorithms developed for visualization can also serve as a general solution to the problem of aggregation in tactical generation (Section 3). We then briefly show the same algorithms at work at the level of text organization, helping to motivate informational structures necessary for constraining page layout and for allocating presentation modalities in the complete page generation scenario (Section 4), We conclude the paper by summarizingthe main points of architectural synthesis that we have pursued and outlining some prominent lines of ongoing work and future development.</Paragraph> </Section> <Section position="4" start_page="0" end_page="15" type="metho"> <SectionTitle> 2 Automatic Diagram Generation using Dependency Lattices </SectionTitle> <Paragraph position="0"> The approach to diagram generation adopted within the KOMET-PAVE experiment has been developed both theoretically and practically. The practical side was originally built as part of an 'Editor's Workbench&quot; aimed at facilitating the work of an editor preparing large-scale publications such as encyclopediae (Rostek, Mthr & Fischer 1994). A range Of flexible automatic visualisation tools (cf. Reichenberger, Kamps & Golovchinsky 1995, Htiser et al. 1995) were developed in this context. To illustrate our discussions below, we will adopt one trial application domain in which the Editor's Workbench has been used and for which a significant knowledge base has been constructedwthat is, the art and art history domain already used as a basis for NLG in Teich & Bateman (1994) and Bateman & Teich (1995). Typical information maintained by this knowledge base involves information about artists (particularly biographical information such as birthdates, dates of working in particular institutions, date s of movements, works of art created, etc.), details of works of art and art movements, as well as pictures and full text representations of several thousand biographies.</Paragraph> <Paragraph position="1"> Visualization in the context of the Editor's Workbench focused on providing a high degree of control over all the visual aspects of its presentations: including layout of information and diagram design. The particular aim of visualization was to be able to present overviews of datasets rather than elaborating on specifics, and. this required methods for discovering regularities in the data thatcould then be used to motivate particular presentation strategies. The theoretical basis for the methods developed is given in detail in Kamps (1997) and rests on a new application of Formal Concept Analysis (FCA: Wille 1982). We now show briefly how FCA allows theconstruction of dependency lattices that support flexible diagram design. We adopt as a simple example the set of 'facts' displayed in the following table. These facts * together show the subject areas, institutions, and time periods in which the shown * artists were active. 2 the one shown above, where the columns represent the domain sets on which the relation is defined and the rows represent the relation tuples. Dependency lattices are a particular kind of concept*lattice as defined in Formal Concept Analysis. FCA starts from the notion of a formal context (G, M, 1) representing the data in which G is a set of objects, M is a set of attributes and I establishes a binary relation between the two sets. I(g, m) is read &quot;object g has property m&quot;if g E (7 and mE M~ Such a context is called a one-valued context. The onevalued context corresponding to the Profession-attribute of our example dataset is shown in the table to the left of Figure 1.</Paragraph> <Paragraph position="2"> The formal Concepts of concern in FCA are defined as consisting of an extension and an intension, where the extension is a subset A of the set of objects G and the intension is a subset B of the set of attributes .M. We call the pair (A, 13) a formal concept if each element of the extension may be assigned each attribute Of the intension. Thus, the pairs ({Gropius, Breuer}; *{Urban Planner, Architect}) and ({A.Albers}, {Designer}) represent concepts with respect tO the example one-valued context of Figure 1. More intuitively, in a formal context concepts represent rectangles of maximum size, completely filled with x's after permutation of rows and columns. The Set of all concepts may be computed effectively using the algorithm &quot;Next Closure&quot; developed by Ganter & Wille (1996). The hierarchy relation &quot;subconcept&quot;, established between the set of concepts, is based on inclusions of the respective extensions and intensions of the concepts. Concretely, a concept (A, 13)isa subconcept of (A*, 13&quot;) if and only ifA C_A* C/~ 13&quot; C 13. The main theorem of concept analysis shows thatthis ,subconcept&quot; relationship represents a complete lattice structure (see Wille 1982). Given all concepts, we may construct the Concept lattice starting from the top concept (th e one that has no superconceptS) ~ and proceed top'down recursively. In each step we must compute the set of direct subconcepts and link them tothe respective superconcept until we reach the greatest lower bound of the lattice itself (the existence of the bounds is always guaranteed if we consider finite input data structures). One efficient implementation of this algorithm is explained in greater detail in Kamps (1997). The corresponding lattice for the one-valued context shown in Figure 1 is shown to the right of the figure. The labelling of the nodes of the lattice makes full use of the dependencies and redundancies that the lattice captures. Elements of the extensions ~e shown moving up the lattice, the extension label for each node consists of just those elements which are added at each node, while the members of the intensions are shown moving down the lattice, again adding just those elements that are new for each node. Thus, for example, the node Simply labelled Gropius, Breuer corresponds to the full formal concept ({Gropius, Breuer}, {Architect, Urban Planner}) since both Gropius and Breuer are added new to the extension at that node, while no new elements are added to the intension ('Architect&quot; and &quot;Urban Planner' are both inherited from the two nodes above in the lattice, where they are already present).</Paragraph> <Paragraph position="4"/> <Section position="1" start_page="10" end_page="10" type="sub_section"> <SectionTitle> 2.2 Howto find functional dependencies in the data </SectionTitle> <Paragraph position="0"> The original table of facts with Which we started above is not a one-valued context: it is a muhivalued context. A multivalued context is a generalisation of a one-valued context that may formally be represented as a quadruple (G, M, W, I) where G, M and I are as before. Here, however, the set of values W of the attributes is not trivial: to identify the value w E W of attribute m C M for an object 9 E G we adopt the notation m(9 ) = w and read this as &quot;attribute m of object g has value w&quot;. Thus relation tables in general, such as the original table above, may all be considered as multivalued contexts.</Paragraph> <Paragraph position="1"> Given an n-ary relation, functional relationships may generally be established between subsets of the n domains. However, we adopt the following particular construction of the dependency context: for the set of objects choose the set Of subsets of two elements of the given multi-valued context P2(G), for the set of attributes choose the set of domains M, and for the connectifig incidence relation choose IN({9, h}, m) :C/C/, re(g) = m(h), so that the resulting dependency context is represented by the triple (P2(G), M, IN). Although this only considers pairwise mappings--that is such functional relationships that hold between two single domains--it simplifies the problem drastically and is a sensible approach for two reasons:- first, the isolated functional relationships may, as we will see, be arranged in the form of a dependency lattice that allows a wholistic view on the dependency structure, and second, it is computatiorially simple to achieve.</Paragraph> <Paragraph position="2"> The underlying principle is then straightforward: compute a (one-valued) dependency Context from the * given n-ary relation table and apply the techniques described above for the construction O f the corresponding dependency lattice. This is illustrated in the table to the left of Figure 2, which shows the dependency context corresponding to our original full table of facts above. An entry in this table indicates that the identified attribute has the same value for both the facts identified in the *object labels of the leftmost column:for example, &quot;gl' and 'g2' share the values of their Professions and Schools attributes. The corresponding dependency lattice, built in the same manner asshown for one-valued contexts, is shown in the lattice on the right of the figure.</Paragraph> <Paragraph position="3"> The arcs in this lattice represent the functional dependencies between the involved domains whereas the equalities (e.g., m(gl )=re(g2)) represent the redundancies that may be observed in the * table: for example, the lower left node labelled Period indicates not only that the third and fourth row entries under Period (g3 and g4) are identical but also, following the upward arc that these entries are equal with respect to School; similarly, following the upward arcs (which is possible because functional dependencies are transitive), ~e middle node (m(gl)=m(g2)) indicates that the first and second row table entries are shared with respect to both School and Profession. The lattice as a whole indicates that there are functional relationships from the set of persons into the set of professions, the set of periods, and the set of schools. A further functional relationship exists from the set of periods into the set of schools. -</Paragraph> </Section> <Section position="2" start_page="10" end_page="12" type="sub_section"> <SectionTitle> 2.3 How dependency lattices are used for visual|sat|on </SectionTitle> <Paragraph position="0"> A dependency lattice, in which the edges represent functions between the domains and the non-existing edges represent set-valuedmappings, may be interpreted as a set of classifications of the relational input. For graphics generation it is imPortant that all domains of the relation become graphically encoded. This means the encoding is complete. To this purpose, Kamps (1997) proposes a graphical encoding algorithm that starts encoding the bottom domain and walks up the *lattice in a bottom-up / left-to-right approach encoding the upper domains. The idea of this model, much abbreviated, is that the cardinality of the bottom domain is the largest, whereas th e domains further up in the lattice contain less and less elements. Thus, the bottom domain is graphically encoded using so:called graphical elements (rectangle, circle, line, etc.), whereas the upper domains are encoded using their graphical attributes (colour, width, radius) as well as set-valued attributes (attachments ofgraphical elements) to keep graphical complexity moderate. Since function~ and set-valued functions are binary relations, the encoding of a structured n-tuple i s composed of a set of binary encodings. In the algorithm proposed by Kamps (1997), each domain is visited and encoded once which * implies one .walk through the lattice representing exactly one classification and one visual|sat|on of the data.</Paragraph> <Paragraph position="1"> Many alternative diagrams may thus be generated for such a data set and the visualization algorithm contains extensive perceptual heuristics for evaluating among these.</Paragraph> <Paragraph position="2"> Figure 3 shows two example diagrams that are produced from the dataset of our example table via the dependency lattice shown to the right of Figure 2. Informally, from the lattice we can see directly that artists ('Person'.) can be classified on one hand according to work period and, on the other hand, jointly according to * school and profession. The 'attribute' person, indicated in the lowest node of the lattice, is first allocated to the basic graphical element 'rectangle'; the individual identities of the set members are given by a graphical attachment: a string giving the art|st'shame. The functional relationship between the set of persons and the set of time period s is then represented by the further graphical attribute of the length of the rectangle.</Paragraph> <Paragraph position="3"> This is motivated by the equivalence of the properties of temporal intervals in the data and the properties of the graphical relationship of spatial 'intervals' on the page. Two paths are then open: first following the functional relatioriship to a set of Schools or to a set of professions. Diagram (a) in Figure 3 adopts the first path and encodes the school relationship by means of the further graphical attribute of the color of the rectangle, followed by a nesting rectangle for the relationship to professions; diagram (b) illustrates the second path, in which the selection of graphical encodings is reversed. Both the selection of color and of nesting rectangles are again motivated by the correspondence between the formal properties of the graphical relations and those of the dependencies observed in the data.</Paragraph> <Paragraph position="5"/> </Section> <Section position="3" start_page="12" end_page="15" type="sub_section"> <SectionTitle> 2.4 The partial equivalence of diagram design and text design </SectionTitle> <Paragraph position="0"> Our brief description of the process of producing alternative diagrams can now be considered from the perspective of producing alternative texts. The selection of particular graphical elements, and the commitments that follow for expressing particular functional dependencies, are closely analogous to decisions that need to be made when generating a text from the given dataset. Indeed, textual representations of the example diagrams may be motivated from the dependency lattice structure by proceeding over all functional groupings and taking into account the position of the equalities in the lattice justas in the diagram generation.</Paragraph> <Paragraph position="1"> For instance, starting from equality rn(gl) = rn(g2) in the lattice, it is sensible to relate the fact that this dependency holds both for the schools and for the professions so that we may connect them in a single sentence: i.e~, 'gl' (concerning Gropius) and 'g2' (concerning Breuer) can be compactly expressed by collapsing their (identical) school and profession attributes. A similar phenomenon holds for grouping re(g3) = re(g4), which is shared by the periods and the schools: here, 'g3' (concerning A. Albers) and 'g4' (concerning J. Albers) may be succinctly expressed by collapsing their identical period and Sch0ol attributes.</Paragraph> <Paragraph position="2"> This would motivate the following approximate textual re-rendering of diagram (b): Anni Albers (who was a designer) and J. Albers (who was an urban planner) both taught at the BMC from !933 until 1949. Moholy-Nagy (who was also an urban planner) taught from 1937 until 1938 at the New Bauhaus. Gropius and Breuer (both architects) were, at partially overlapping times (1937-1951 and 1937-1946 respectively), at Harvard. Hilberseimer (who was an architect too) taught at the !IT from 1938 until 1967.</Paragraph> <Paragraph position="3"> In contrast, the other three groupings (indicated by the equalities on the profession node in the lattice) are &quot;simple&quot;--i.e., not shared by more than one domain--s0 that selecting these does not result in a further compaction of a text being possible.</Paragraph> <Paragraph position="4"> 3 Towards a general treatment of aggregation for NLG The extraction of partial commonalities held constant over subsets of the data to be presented--be they expressed via an allocation of common graphical elements Or by textual groupings--is naturally similar to one aspect of the problem of aggregation in NLG. In fact, the functional redundancies that are captured by the lattice 'construction technique are also precisely those redundances that indicate opportunities for structurally-induced aggregation: Selecting a particular graphical element or attribute to realize some aspect of the data is an aggregation step. In this section, we show this in terms more familiar to NLG by briefly sketching how the approach handles one example * of aggregation discussed in the literature: the production of concise telephone network planning reports illustrated by McKeown, Robin & Kukich (1995).</Paragraph> <Paragraph position="5"> One example from McKeown et al. (1995) concerns the data shown in Figure * 4, again re-represented in tabular form. The attributes taken here are the semantic roles that might be used to provide input concerning 3 individual 'facts' (gl, g2, g3) to a tactical generation component. We consider the problem of providing possible 'aggregations' of these facts* in order to improve the resulting sentences that would be generated.</Paragraph> <Paragraph position="6"> This is managed by means of the corresponding dependency lattice, which we also show in Figure 4, abbreviated and annotated somewhat here for ease of discussion. Analogously to the case for diagram generation. where several diagrams may be generated from a single lattice, a dependency lattice represents not a particular aggregation, but rather all possible aggregations in a single compact form. Input expressions for tactical generation can be constructed by working upwards fr0rn the bottom of the lattice. Each node with associated functional dependencies represents a point of possible aggregation.</Paragraph> <Paragraph position="7"> In the diagram, therefore, the lowest nodes in the lattice represent three starting points; from left tO right: (i) aggregations of type, source and destination with respect to the major dimensions of actor, process, etc., and (ii) and (iii) source an.d destination with respect to a type. The righthand Type node then represents aggregation withrespect to the major dimensions analogously to the *left hand node. Respecting-these dependencies results in the following maximally compact rendering of this information: It requested placement in the second quarter of 1995 of a 48-fiber cable from CO to 1103 and 24-fiber from 1201 to 1301 and from 1401 to 1501.</Paragraph> <Paragraph position="8"> * Thus, the dependency lattice directly determines the logical dependency structure of the clause (cf. Halliday 1994).</Paragraph> <Paragraph position="9"> * As McKeown et al. (1995) note, however, it is Sometimes ill-advised to carry out amaximal aggregation.</Paragraph> <Paragraph position="10"> We can also model this restraint using the dependency lattice by bringing more generic (higher) nodes down and 'distributing' them over lower lattice nodes. The motivation for such lowering is typically to be found in registerial constraints and the method of textual development being used in the text at hand. If the 'objects' of the domain (e.g., in this Case, the cable) are to remain salient, then these can be re-distributed from the uppermost n0d~ to enforce redundant expression; for example: It requested placement ...of a 48-fiber cable from CO to 1103 and 24-fiber cables from 1201 to 1301 and from 1401 to 1501 The other examples presented by McKeown et al. (1995), as Well as *other examples of similar phenomena presented in the literature (e.g, Dalianis & Hovy 1996) are handled similarly. * Since the dependency lattice does not itself determine which of the possible aggregations is taken up, but simply represents what is possible, this approach turns aggregation into a process of communicative choice along exactly the same lines as all other choices in the grammar, semantics, text organization, etc. One of the major benefits of the dependencY * lattice is then to represent this space of possibilities compactly, allowing a more systematic ,weighing of alternatives. The possibilities for aggregation captured by a dependency * lattice then largely remove the need for ad hoc specific rules of grouping. Nevertheless, the extraction of those *dimensions of organization or aggregation that are particularly relevant for a specific text or diagram can only-be determined from the communicative purpose of the text or diagram that is being constructed: i.e., which &quot;question' is the text/diagram answering. Therefore, the kinds of grouping and organization that we have illustrated in the paper so far cannot replace communicative-goal driven NLG; they need rather to be properly integrated in a goal-driven architecture. This we illustrate in the section following.</Paragraph> <Paragraph position="11"> Within the KOMET-PAVE page generation experiment, we attempted to make use of the close analogies we have illustrated above between data-driven aggregation for diagram design and for text production. Moreover, the existence of a general aggregation tool allows us to consider aggregation as a general property of all levels of linguistic representation constructed during the generation process. The lattice construction algorithm is robust and fast and we are now *aiming to construct a dependency lattice after the production of each level of structure during generation. This should apply to grammar and rhetorical structure as well as * to the more semantic or domain oriented aggregations discussed above. In our final example in this paper, therefore, we briefly sketch the utility of performing data-driven aggregation on the results of a text planning process aimed at producing rhetorically motivated page specifications.</Paragraph> <Paragraph position="12"> The purpose of the KOMET-PAVE experiment was to provide a system where the response of the system to a user's request for information is a single 'page' of information combining generated text, generated * graphics, and retrieved visual information (pictures, etc.) within a communicative-functionally motivated * layout. The multimedia page is therefore seen as the basic unit of information presentation, while these units are themselves seen as moves in a multimodal dialogue (cf. Stein & Thiel 1993); the analogy to (and extension of) web-based information services should be obvious~ Given our use of the art and art history domain, the particular goal of the pages generated by the system was to present useful 'starting-off points', or overviews, of the information maintained in the knowledge base. Our example in this section concerns possible answers of the system to a question concerning the spread of the Bauhaus movement. The input to the page synthesis process was taken as a set of artists selected during the previous 'conversational move' and some generic features determined for such pages) When planning the information to be expressed by a page as a whole, it is possible to construct an RST-like structure as is familiar from NLG for individual texts (e.g., Hovy, Lavid, Maier, Mittal & Paris 1992; Moore & Paris 1993)--indeed, prior to further information chunking, the structure could well be a single text. An example of such a structure is shown on the left of Figure 54 We assume that generic constraints on this type of text predispose the planning system to pursue presentations of evidence for assertions made and, at almost any excuse, short biographies of any artists mentioned as additional background.</Paragraph> <Paragraph position="13"> The information present in this RST-structure can be made amenable to formal concept analysis in a number of ways; it is simply necessary to make available the relations and their arguments so that the data is structured as in our example s above. Then, constructing a dependency lattice on the basis of this information yields a number of possible aggregations: most useful here are two sets of functional dependencies, one grouping the acts of teaching * around the predicate of teaching and one grouping the biographies. These points of aggregation in effect 're-structure' the corresponding RST, as shown to the right of Figure 5. This restructuring factors out commonalities so that information from lower leaves of the tree has been placed at higher branches. This results in an altemative, more richly structured presentation plan, the leaves of which are then analyzed in order to estimate how appropriate particular realizations and media-allocations would be. &quot; We have already *seen some results of attempting further realization of the set of teaching facts since our original starting table in Section 2 was just such a set. Diagrams such as those in ~Eigure 3 can readily be produced, whereas the corresponding texts (see above) are not particularly smooth. We account for this by considering many co-varying dimensions of functional dependencies,, as in the combined nucleus of 3The Bauhaus exanaple is taken from Kamps, H~ser, Mrhr & Schmidt's (1996) discussion of interface design and the kinds of interaction that a multimodal information system should support. Several examples of pages actually generated by the system are available on the web at URE: ' http : //www. darmstadt .gmd. de/publ ish/komet/kometpave-pics- 96. html'.</Paragraph> <Paragraph position="14"> The presentation environment is implemented in Smalltalk, the visualization and layout engines in C; the text generation component in Common Lisp; page generation is in real-time.</Paragraph> <Paragraph position="15"> ~oration: e.g.</Paragraph> <Paragraph position="16"> &quot;One means by which the Bauhaus spread was by Bauhaus members migrating to the US and teaching Bauhaus methods.&quot; vidence</Paragraph> <Paragraph position="18"> &quot;One means by which the Bauhaus background spread was by Bauhaus migrating to nt the US and teaching deg . - : deg , bio bto blo methods.'&quot; g &quot;'People &quot;'X &quot;Y I who taught taught did this at at ! include ... from .. from I X, Y, ...'&quot; ... to...&quot; ... to...&quot; I .Figure 5: RST-like structuring Of the Contents of a potential page: before and after aggregation * the first embedded elaboration, to more strongly motivate a diagram. 5 This then serves as the input for the visualization process described above resulting in, for example, a timeline diagram. In contrast, the dependency lattices constructed for the individual biographies exhibit far fewer dimensions of reoccuring commonalities (e.g., simple progression in time with accompanying changes in location or state revolving around a single individual) and so are considered good candidates for textual expression. And, indeed, texts appropriate for these chunks of information are in fact precisely the simple biographies produced by the genre-driven text generation component described previously in Bateman & Teich (1995). Finally, passing the revised RST-structure on to layout planning (cf. Reichenberger, Rondhuis, Kleinz & Bateman 1996), complete with its leaves filled in with text-and diagrams as motivated here, results in a synthesized multimedia page with communicatively appropriate layout as required.</Paragraph> </Section> </Section> class="xml-element"></Paper>