Abstract
Automatic keyphrase generation can help human efficiently understand or process critical information from massive social media posts. Seq2Seq-based generation models that can produce both present and absent keyphrases have achieved remarkable performance. However, existing models are limited by the sparseness of posts that are widely exhibited in social media language. Sparseness makes it difficult for models to obtain useful features and generate accurate keyphrases. To address this problem, we propose the Topic and Reference Guided Keyphrase Generation model(TRGKG) for social media posts, which enrich scarce posts features by corpus-level topics and post-level reference knowledge. The proposed model incorporates a contextual neural topic model to exploit topics and a heterogeneous graph to capture reference knowledge from retrieved related posts. To guide the decoding process, we introduce new topic-aware hierarchical attention and copy mechanism, which directly copies appropriate words from both the source post and its references. Experiments on two public datasets demonstrate that TRGKG achieves state-of-the-art performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, J., Zhang, X., Wu, Y., Yan, Z., Li, Z.: Keyphrase generation with correlation constraints. In: Empirical Methods in Natural Language Processing (2018)
Chen, W., Gao, Y., Zhang, J., King, I., Lyu, M.R.: Title-guided encoding for keyphrase generation. In: National Conference on Artificial Intelligence (2019)
Cho, K., van Merriënboer, B., Gulcehre, C.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: EMNLP (2014)
He, Q., Yang, J., Shi, B.: Constructing knowledge graph for social networks in a deep and holistic way. In: Companion Proceedings of the Web Conference (2020)
Kim, J., Jeong, M., Choi, S., won Hwang, S.: Structure-augmented keyphrase generation. In: Empirical Methods in Natural Language Processing (2021)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)
Meng, R., Yuan, X., Wang, T., Zhao, S., Trischler, A., He, D.: An empirical study on neural keyphrase generation. In: NAACL (2021)
Meng, R., Zhao, S., Han, S., He, D., Brusilovsky, P., Chi, Y.: Deep keyphrase generation. In: Meeting of the Association for Computational Linguistics (2017)
Meng, X., Wei, F., Liu, X., Zhou, M., Li, S., Wang, H.: Entity-centric topic-oriented opinion summarization in twitter. In: Knowledge Discovery and Data Mining (2012)
Miao, Y., Grefenstette, E., Blunsom, P.: Discovering discrete latent topics with neural variational inference. Computation and Language. arXiv preprint arXiv:1706.00359 (2017)
Mihalcea, R., Tarau, P.: Textrank: Bringing order into text. In: Empirical Methods in Natural Language Processing (2004)
Panwar, M., Shailabh, S., Aggarwal, M., Krishnamurthy, B.: Tan-ntm: topic attention networks for neural topic modeling. In: ACL (2021)
Ruths, D., Pfeffer, J.: Social media for large studies of behavior. Science 1063–1064 (2014)
Song, K., Tan, X., Qin, T., Lu, J., Liu, T.Y.: Mpnet: masked and permuted pre-training for language understanding. Computation and Language. arXiv preprint arXiv:2004.09297 (2020)
Vaswani, A., et al.: Attention is all you need. In: Neural Information Processing Systems (2017)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. STAT 20 (2017)
Wang, Y., Li, J., Chan, H.P., King, I., Lyu, M.R., Shi, S.: Topic-aware neural keyphrase generation for social media language. In: ACL (2019)
Wang, Y., Li, J., King, I., Lyu, M.R., Shi, S.: Microblog hashtag generation via encoding conversation contexts. In: North American Chapter of the Association for Computational Linguistics (2019)
Ye, J., Cai, R., Gui, T., Zhang, Q.: Heterogeneous graph neural networks for keyphrase generation. Computation and Language. arXiv preprint arXiv:2109.04703 (2021)
Ye, J., Gui, T., Luo, Y., Xu, Y., Zhang, Q.: One2set: generating diverse keyphrases as a set. In: Meeting of the Association for Computational Linguistics (2021)
Yuan, X., et al.: One size does not fit all: Generating and evaluating variable number of keyphrases. In: Meeting of the Association for Computational Linguistics (2020)
Zhang, Q., Wang, Y., Gong, Y., Huang, X.: Keyphrase extraction using deep recurrent neural networks on twitter. In: EMNLP (2016)
Acknowledgements
This work is supported by the National Key R &D Program of China under Grants (No. 2018YFB0204300).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, X., Chen, X., Huang, Z., Dou, Y., Hu, B. (2022). Topic and Reference Guided Keyphrase Generation from Social Media. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-10986-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10985-0
Online ISBN: 978-3-031-10986-7
eBook Packages: Computer ScienceComputer Science (R0)