SumTitles: a Summarization Dataset with Low Extractiveness
Valentin Malykh, Konstantin Chernis, Ekaterina Artemova, Irina Piontkovskaya
Abstract
The existing dialogue summarization corpora are significantly extractive. We introduce a methodology for dataset extractiveness evaluation and present a new low-extractive corpus of movie dialogues for abstractive text summarization along with baseline evaluation. The corpus contains 153k dialogues and consists of three parts: 1) automatically aligned subtitles, 2) automatically aligned scenes from scripts, and 3) manually aligned scenes from scripts. We also present an alignment algorithm which we use to construct the corpus.- Anthology ID:
- 2020.coling-main.503
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5718–5730
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.503
- DOI:
- 10.18653/v1/2020.coling-main.503
- Cite (ACL):
- Valentin Malykh, Konstantin Chernis, Ekaterina Artemova, and Irina Piontkovskaya. 2020. SumTitles: a Summarization Dataset with Low Extractiveness. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5718–5730, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- SumTitles: a Summarization Dataset with Low Extractiveness (Malykh et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.503.pdf
- Data
- WikiHow