Extractive summarization by aggregating multiple similarities
Paper in proceeding, 2015

News reports, social media streams, blogs, digitized archives and books are part of a plethora of reading sources that people face every day. This raises the question of how to best generate automatic summaries. Many existing methods for extracting summaries rely on comparing the similarity of two sentences in some way. We present new ways of measuring this similarity, based on sentiment analysis and continuous vector space representations, and show that combining these together with similarity measures from existing methods, helps to create better summaries. The finding is demonstrated with MULTSUM, a novel summarization method that uses ideas from kernel methods to combine sentence similarity measures. Submodular optimization is then used to produce summaries that take several different similarity measures into account. Our method improves over the state-of-the-art on standard benchmark datasets; it is also fast and scale to large document collections, and the results are statistically significant.

Author

Olof Mogren

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Mikael Kågebäck

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

International Conference Recent Advances in Natural Language Processing, RANLP

13138502 (ISSN)

Vol. 2015 451-457

Subject Categories

Language Technology (Computational Linguistics)

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