@inproceedings{linhares-pontes-etal-2018-multi,
title = "Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming",
author = "Linhares Pontes, Elvys and
Huet, St{\'e}phane and
Gouveia da Silva, Thiago and
Linhares, Andr{\'e}a Carneiro and
Torres-Moreno, Juan-Manuel",
editor = "Glava{\v{s}}, Goran and
Somasundaran, Swapna and
Riedl, Martin and
Hovy, Eduard",
booktitle = "Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing ({T}ext{G}raphs-12)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1704",
doi = "10.18653/v1/W18-1704",
pages = "18--27",
abstract = "Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-gram scores to generate 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 dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.",
}
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<abstract>Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-gram scores to generate 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 dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.</abstract>
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%0 Conference Proceedings
%T Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming
%A Linhares Pontes, Elvys
%A Huet, Stéphane
%A Gouveia da Silva, Thiago
%A Linhares, Andréa Carneiro
%A Torres-Moreno, Juan-Manuel
%Y Glavaš, Goran
%Y Somasundaran, Swapna
%Y Riedl, Martin
%Y Hovy, Eduard
%S Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F linhares-pontes-etal-2018-multi
%X Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-gram scores to generate 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 dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.
%R 10.18653/v1/W18-1704
%U https://aclanthology.org/W18-1704
%U https://doi.org/10.18653/v1/W18-1704
%P 18-27
Markdown (Informal)
[Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming](https://aclanthology.org/W18-1704) (Linhares Pontes et al., TextGraphs 2018)
ACL