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Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data

Tauchmann, Christopher and Arnold, Thomas and Hanselowski, Andreas and Meyer, Christian M. and Mieskes, Margot (2018):
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data.
In: Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC), European Language Resources Association, Miyazaki, Japan, [Online-Edition: http://www.lrec-conf.org/proceedings/lrec2018/summaries/252....],
[Conference or Workshop Item]

Abstract

Automatic summarization has so far focused on datasets of ten to twenty rather short documents of mostly news articles. But automatic systems could in theory analyze hundreds of documents from a range of sources and provide an overview to the interested reader. Such a summary would ideally present the most general issues in a specific topic and allow for more in-depth information on specific aspects within said topic. In this paper, we present a new approach for creating hierarchical summarization corpora by first, extracting relevant content from large, heterogeneous document collections using crowdsourcing and second, ordering the relevant information hierarchically by trained annotators. Our resulting corpus can be used to develop and evaluate hierarchical summarization systems.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Tauchmann, Christopher and Arnold, Thomas and Hanselowski, Andreas and Meyer, Christian M. and Mieskes, Margot
Title: Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data
Language: English
Abstract:

Automatic summarization has so far focused on datasets of ten to twenty rather short documents of mostly news articles. But automatic systems could in theory analyze hundreds of documents from a range of sources and provide an overview to the interested reader. Such a summary would ideally present the most general issues in a specific topic and allow for more in-depth information on specific aspects within said topic. In this paper, we present a new approach for creating hierarchical summarization corpora by first, extracting relevant content from large, heterogeneous document collections using crowdsourcing and second, ordering the relevant information hierarchically by trained annotators. Our resulting corpus can be used to develop and evaluate hierarchical summarization systems.

Title of Book: Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)
Publisher: European Language Resources Association
Uncontrolled Keywords: reviewed;AIPHES_corpus;AIPHES_area_c1
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
Event Location: Miyazaki, Japan
Date Deposited: 14 Dec 2017 14:24
Official URL: http://www.lrec-conf.org/proceedings/lrec2018/summaries/252....
Identification Number: TUD-CS-2018-0007
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