Semi-Supervised Learning for Neural Keyphrase Generation

Hai Ye, Lu Wang

[How to correct problems with metadata yourself]


Abstract
We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich domains. In this paper, we propose semi-supervised keyphrase generation methods by leveraging both labeled data and large-scale unlabeled samples for learning. Two strategies are proposed. First, unlabeled documents are first tagged with synthetic keyphrases obtained from unsupervised keyphrase extraction methods or a self-learning algorithm, and then combined with labeled samples for training. Furthermore, we investigate a multi-task learning framework to jointly learn to generate keyphrases as well as the titles of the articles. Experimental results show that our semi-supervised learning-based methods outperform a state-of-the-art model trained with labeled data only.
Anthology ID:
D18-1447
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4142–4153
Language:
URL:
https://aclanthology.org/D18-1447
DOI:
10.18653/v1/D18-1447
Bibkey:
Cite (ACL):
Hai Ye and Lu Wang. 2018. Semi-Supervised Learning for Neural Keyphrase Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4142–4153, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Semi-Supervised Learning for Neural Keyphrase Generation (Ye & Wang, EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D18-1447.pdf
Data
KP20k