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Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization

Falke, Tobias ; Gurevych, Iryna (2019)
Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization.
The 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019). Minneapolis, USA (02.06.2019--07.10.2019)
doi: 10.18653/v1/N19-1074
Conference or Workshop Item, Bibliographie

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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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Falke, Tobias ; Gurevych, Iryna
Type of entry: Bibliographie
Title: Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization
Language: English
Date: 27 February 2019
Place of Publication: Minneapolis, Minnesota
Publisher: Association for Computational Linguistics
Book Title: 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)
Event Title: The 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)
Event Location: Minneapolis, USA
Event Dates: 02.06.2019--07.10.2019
DOI: 10.18653/v1/N19-1074
URL / URN: https://aclanthology.org/N19-1074
Corresponding Links:
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.

Uncontrolled Keywords: AIPHES;UKP_reviewed
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Date Deposited: 18 Sep 2019 12:13
Last Modified: 11 Jun 2024 07:17
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