Abstract
This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. We propose novel structure-augmented document encoding approaches that consist of the following two phases: The first phase, generating structure, extends the given document with related but absent keyphrases, augmenting missing context. The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text. Our empirical results validate that our proposed structure augmentation and augmentation-aware encoding/decoding can improve KG for both scenarios, outperforming the state-of-the-art.- Anthology ID:
- 2021.emnlp-main.209
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2657–2667
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.209
- DOI:
- 10.18653/v1/2021.emnlp-main.209
- Cite (ACL):
- Jihyuk Kim, Myeongho Jeong, Seungtaek Choi, and Seung-won Hwang. 2021. Structure-Augmented Keyphrase Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2657–2667, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Structure-Augmented Keyphrase Generation (Kim et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.emnlp-main.209.pdf
- Code
- jihyukkim-nlp/straugkg
- Data
- KP20k