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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D17-1320.pdf
- Code
- UKPLab/emnlp2017-cmapsum-corpus