Structure-Augmented Keyphrase Generation

Jihyuk Kim, Myeongho Jeong, Seungtaek Choi, Seung-won Hwang


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.209.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.209.mp4
Code
 jihyukkim-nlp/straugkg
Data
KP20k