Abstract
In this paper, we present an overview of the NLPCC 2022 shared task on Dialogue Texts Analysis (DTA). The evaluation consists of two sub-tasks: (1) Dialogue Topic Extraction (DTE) and (2) Dialogue Summary Generation (DSG). We manually annotated a large-scale corpus for DTA, in which each dialogue contains customer and service conversation. A total of 50 + teams participated in the DTA evaluation task. We believe that DTA will push forward the research in the field of dialogue text analysis.
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
Trivedi, A., Pant, N., Shah, P., Sonik, S., Agrawal, S.: Speech to text and text to speech recognition systems-a review. IOSR J. Comput. Eng 20(2), 36–43 (2018)
Wang, L., Yao, J., Tao, Y., Zhong, L., Liu, W., Du, Q.: A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization. In: IJCAI 2018: Proceedings of the 27th International Joint Conference on Artificial Intelligence
El-Kassas, W.S., Salama, C., Rafea, A., Mohamed, H.K.: Automatic text summarization: a Comprehensive Survey. Expert Systems With Applications (2020)
Liu, J., et al.: Topic-aware contrastive learning for abstractive dialogue summarization. In: Findings of the Association for Computational Linguistics: EMNLP (2021)
Xu, Y., Zhao, H., Zhang, Z.: Topic-aware multi-turn dialogue modeling. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021) (2021)
Zou, Y., et al.: Topic-Oriented spoken dialogue summarization for customer service with saliency-aware topic modeling. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 14665–14673 (2021)
Mihalcea, R., Tarau, P.: Textrank: bringing order into text. In: Proceedings of the Empirical Methods in Natural Language Processing (2004)
Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (2016)
Nallapati, R., Zhai, F., Zhou, B.: SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation (1997)
Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Liu, Z., Ng, A., Lee, S., Aw, A., Chen, N.F.: Topic-aware pointer-generator networks for summarizing spoken conversations. In: Proceedings of the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Goo, C.W., Chen, Y.N.: Abstractive dialogue summarization with sentence-gated modeling optimized by dialogue acts. In: Proceedings of the 2018 IEEE Spoken Language Technology Workshop (SLT)
Chen, J., Yang, D.: Multi-View sequence-to-sequence models with conversational structure for abstractive dialogue summarization. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Lin C Y . ROUGE: Recall-Oriented Understudy for Gisting Evaluation (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Miao, Q., Guan, T., Yang, Y., Zhang, Y., Xu, H., Ge, F. (2022). Overview of the NLPCC 2022 Shared Task: Dialogue Text Analysis (DTA). In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13552. Springer, Cham. https://doi.org/10.1007/978-3-031-17189-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-17189-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17188-8
Online ISBN: 978-3-031-17189-5
eBook Packages: Computer ScienceComputer Science (R0)
