Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps

Tobias Falke, Iryna Gurevych


Abstract
Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.
Anthology ID:
D17-1320
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2951–2961
Language:
URL:
https://www.aclweb.org/anthology/D17-1320
DOI:
10.18653/v1/D17-1320
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1320.pdf