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://aclanthology.org/D17-1320
DOI:
10.18653/v1/D17-1320
Bibkey:
Cite (ACL):
Tobias Falke and Iryna Gurevych. 2017. Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2951–2961, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps (Falke & Gurevych, EMNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D17-1320.pdf
Code
 UKPLab/emnlp2017-cmapsum-corpus