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        <title>Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization</title>
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            <namePart type="given">Jill</namePart>
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    <abstract>Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.</abstract>
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%0 Conference Proceedings
%T Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
%A Falke, Tobias
%A Gurevych, Iryna
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F falke-gurevych-2019-fast
%X Concept map-based multi-document summarization has recently been proposed as a variant of the traditional summarization task with graph-structured summaries. As shown by previous work, the grouping of coreferent concept mentions across documents is a crucial subtask of it. However, while the current state-of-the-art method suggested a new grouping method that was shown to improve the summary quality, its use of pairwise comparisons leads to polynomial runtime complexity that prohibits the application to large document collections. In this paper, we propose two alternative grouping techniques based on locality sensitive hashing, approximate nearest neighbor search and a fast clustering algorithm. They exhibit linear and log-linear runtime complexity, making them much more scalable. We report experimental results that confirm the improved runtime behavior while also showing that the quality of the summary concept maps remains comparable.
%R 10.18653/v1/N19-1074
%U https://aclanthology.org/N19-1074
%U https://doi.org/10.18653/v1/N19-1074
%P 695-700

Markdown (Informal)

[Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization](https://aclanthology.org/N19-1074) (Falke & Gurevych, NAACL 2019)

• Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization (Falke & Gurevych, NAACL 2019)

ACL

• Tobias Falke and Iryna Gurevych. 2019. Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 695–700, Minneapolis, Minnesota. Association for Computational Linguistics.