Extractive Summarization using Continuous Vector Space Models
Paper in proceedings, 2014

Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.

Author

Mikael Kågebäck

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

Olof Mogren

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

Nina Tahmasebi

Chalmers, Computer Science and Engineering (Chalmers)

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers)

Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC) EACL, April 26-30, 2014 Gothenburg, Sweden

31-39

Subject Categories

Computer Science

ISBN

978-1-937284-94-7