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Identifying Non-Explicit Citing Sentences for Citation-Based Summarization

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If you have a question about this talk, please contact Jimme Jardine .

http://www.aclweb.org/anthology/P/P10/P10-1057.pdf

Identifying background (context) information in scientiļ¬c articles can help scholars understand major contributions in their research area more easily. In this paper, we propose a general framework based on probabilistic inference to extract such context information from scientiļ¬c papers. We model the sentences in an article and their lexical similarities as a Markov Random Field tuned to detect the patterns that context data create, and employ a Belief Propagation mechanism to detect likely context sentences. We also address the problem of generating surveys of scientiļ¬c papers. Our experiments show greater pyramid scores for surveys generated using such context information rather than citation sentences alone

This talk is part of the Natural Language Processing Reading Group series.

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