The combination of web document contents, sentences and users’ comments from social networks provides a viewpoint of a web document towards a special event. This paper proposes a framework named SoRTESum to take advantage of information from Twitter viz. Diversity and reflection of document content to generate high-quality summaries by a novel sentence similarity measurement. The framework first formulates sentences and tweets by recognizing textual entailment (RTE) relation to incorporate social information. Next, they are modeled in a Dual Wing Entailment Graph, which captures the entailment relation to calculate the sentence similarity based on mutual reinforcement information. Finally, important sentences and representative tweets are selected by a ranking algorithm. By incorporating social information, SoRTESum obtained improvements over state-of-the-art unsupervised baselines e.g., Random, SentenceLead, LexRank of 0.51 %-8.8% of ROUGE-1 and comparable results with strong supervised methods e.g., L2R and CrossL2R trained by RankBoost for single-document summarization.
CITATION STYLE
Nguyen, M. T., & Nguyen, M. L. (2016). SoRTESum: A social context framework for single-document summarization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9626, pp. 3–14). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_1
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