Extractive Summarization By Maximizing Semantic Volume

Abstract

The most successful approaches to extractive text summarization seek to maximize bigram coverage subject to a budget constraint. In this work, we propose instead to maximize semantic volume. We embed each sentence in a semantic space and construct a summary by choosing a subset of sentences whose convex hull maximizes volume in that space. We provide a greedy algorithm based on the Gram-Schmidt process to efficiently perform volume maximization. Our method outperforms the state-of-the-art summarization approaches on benchmark datasets.

Publication Date

1-1-2015

Publication Title

Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Number of Pages

1961-1966

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.18653/v1/d15-1228

Socpus ID

84959867982 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84959867982

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