Jahna Otterbacher
Güneş Erkan
ABSTRACT
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. "How many victims have been found?") 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 TRDR score that is significantly higher than that of the baseline.
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