Extractive Headline Generation Based on Learning to Rank for Community Question Answering

Tatsuru Higurashi, Hayato Kobayashi, Takeshi Masuyama, Kazuma Murao


Abstract
User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks. In such cases, we cannot use paired supervised data, e.g., pairs of articles and headlines, to learn a headline generation model. To overcome this problem, we propose an extractive headline generation method based on learning to rank for CQA that extracts the most informative substring from each question as its headline. Experimental results show that our method outperforms several baselines, including a prefix-based method, which is widely used in real services.
Anthology ID:
C18-1148
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1742–1753
URL:
https://www.aclweb.org/anthology/C18-1148
DOI:
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