Topic-Aware Neural Keyphrase Generation for Social Media Language

Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, Shuming Shi

Abstract
A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient information from massive posts. While most existing methods extract words from source posts to form keyphrases, we propose a sequence-to-sequence (seq2seq) based neural keyphrase generation framework, enabling absent keyphrases to be created. Moreover, our model, being topic-aware, allows joint modeling of corpus-level latent topic representations, which helps alleviate data sparsity widely exhibited in social media language. Experiments on three datasets collected from English and Chinese social media platforms show that our model significantly outperforms both extraction and generation models without exploiting latent topics. Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
Anthology ID:
P19-1240
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2516–2526
Language:
URL:
https://aclanthology.org/P19-1240
DOI:
10.18653/v1/P19-1240
Bibkey:
Cite (ACL):
Yue Wang, Jing Li, Hou Pong Chan, Irwin King, Michael R. Lyu, and Shuming Shi. 2019. Topic-Aware Neural Keyphrase Generation for Social Media Language. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2516–2526, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Topic-Aware Neural Keyphrase Generation for Social Media Language (Wang et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P19-1240.pdf
Code
 yuewang-cuhk/TAKG +  additional community code
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