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<Paper uid="W03-1203">
  <Title>Combining Optimal Clustering and Hidden Markov Models for Extractive Summarization</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We propose Hidden Markov models with unsupervised training for extractive summarization. Extractive summarization selects salient sentences from documents to be included in a summary. Unsupervised clustering combined with heuristics is a popular approach because no annotated data is required. However, conventional clustering methods such as K-means do not take text cohesion into consideration.</Paragraph>
    <Paragraph position="1"> Probabilistic methods are more rigorous and robust, but they usually require supervised training with annotated data. Our method incorporates unsupervised training with clustering, into a probabilistic framework. Clustering is done by modified K-means (MKM)--a method that yields more optimal clusters than the conventional K-means method. Text cohesion is modeled by the transition probabilities of an HMM, and term distribution is modeled by the emission probabilities.</Paragraph>
    <Paragraph position="2"> The final decoding process tags sentences in a text with theme class labels. Parameter training is carried out by the segmental K-means (SKM) algorithm. The output of our system can be used to extract salient sentences for summaries, or used for topic detection. Content-based evaluation shows that our method outperforms an existing extractive summarizer by 22.8% in terms of relative similarity, and outperforms a baseline summarizer that selects the top N sentences as salient sentences by 46.3%.</Paragraph>
  </Section>
class="xml-element"></Paper>