<?xml version="1.0" standalone="yes"?>
<Paper uid="H05-1115">
  <Title>Using Random Walks for Question-focused Sentence Retrieval</Title>
  <Section position="1" start_page="2" end_page="2" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. Annotators generated a list of questions central to understanding each story in our corpus. Because of the dynamic nature of the stories, many questions are time-sensitive (e.g.</Paragraph>
    <Paragraph position="1"> &amp;quot;How many victims have been found?&amp;quot;) Judges found sentences providing an answer to each question. To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to the input question via IDF-weighted word overlap. In our experiments, the method achieves a TRDRscore that is significantly higher than that of the baseline.</Paragraph>
  </Section>
class="xml-element"></Paper>