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
- 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)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/D18-1447.pdf
- Data
- KP20k