COLING 2020main9 citations
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.
BibTeX
@inproceedings{malykh-etal-2020-sumtitles,
title = "{S}um{T}itles: a Summarization Dataset with Low Extractiveness",
author = "Malykh, Valentin and
Chernis, Konstantin and
Artemova, Ekaterina and
Piontkovskaya, Irina",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.503/",
doi = "10.18653/v1/2020.coling-main.503",
pages = "5718--5730"
}