A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming

Authors

  • Elvys Linhares Pontes L3i, La Rochelle Université, La Rochelle, France LIA, Avignon Université, Avignon, France Polytechnique Montréal, Montréal, Canada
  • Stéphane Huet LIA, Avignon Université, Avignon, France
  • Juan Manuel Torres Moreno LIA, Avignon Université, Avignon, France Polytechnique Montréal, Montréal, Canada
  • Thiago Gouveia da Silva Inst. Federal de Educação, Ciência e Tecnologia da Paraiba, PB, Brazil, Instituto de Computação Univ. Federal Fluminense, RJ, Brazil
  • Andréa Carneiro Linhares Universidade Federal do Ceará, Sobral, Brazil

DOI:

https://doi.org/10.13053/cys-24-2-3335

Keywords:

Multi-sentence compression, integer linear programming, word graph

Abstract

Multi-Sentence Compression (MSC) aims to generate a short sentence with the key information from a cluster of similar sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes an Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, with the goal of generating more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state of the art for evaluations led on news datasets in three languages: French, Portuguese and Spanish. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. In additional tests, which take advantage of the fact that the length of compressions can be modulated, we still improve ROUGE scores with shorter output sentences.

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Published

2020-06-23

Issue

Vol. 24 No. 2 (2020): Thematic Issue on Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez)

Section

Articles

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