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        <title>Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization</title>
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    <abstract>Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps. In this work, we propose a new model for the task that addresses several issues in previous methods. It learns to identify and merge coreferent concepts to reduce redundancy, determines their importance with a strong supervised model and finds an optimal summary concept map via integer linear programming. It is also computationally more efficient than previous methods, allowing us to summarize larger document sets. We evaluate the model on two datasets, finding that it outperforms several approaches from previous work.</abstract>
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%0 Conference Proceedings
%T Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization
%A Falke, Tobias
%A Meyer, Christian M.
%A Gurevych, Iryna
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F falke-etal-2017-concept
%X Concept-map-based multi-document summarization is a variant of traditional summarization that produces structured summaries in the form of concept maps. In this work, we propose a new model for the task that addresses several issues in previous methods. It learns to identify and merge coreferent concepts to reduce redundancy, determines their importance with a strong supervised model and finds an optimal summary concept map via integer linear programming. It is also computationally more efficient than previous methods, allowing us to summarize larger document sets. We evaluate the model on two datasets, finding that it outperforms several approaches from previous work.
%U https://aclanthology.org/I17-1081
%P 801-811

Markdown (Informal)

[Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization](https://aclanthology.org/I17-1081) (Falke et al., IJCNLP 2017)

• Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization (Falke et al., IJCNLP 2017)

ACL

• Tobias Falke, Christian M. Meyer, and Iryna Gurevych. 2017. Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 801–811, Taipei, Taiwan. Asian Federation of Natural Language Processing.