Extractive summarization using continuous vector space models

Mikael Kågebäck, Olof Mogren, Nina Tahmasebi, Devdatt Dubhashi, (EACL 2014 workshop on continuous vector space models and their compositionality (CVSC))
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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.

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Our implementation of submodular optimization is available here and the recursive neural network used in the paper is based on the code made available by Richard Socher on his webpage.


  title={Extractive summarization using continuous vector space models},
  author={K{aa}geb{"a}ck, Mikael and Mogren, Olof and Tahmasebi, Nina and Dubhashi, Devdatt},
  booktitle={Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)@ EACL},