Abstract
Automatic email subject generation is of great significance to both the recipient and the email system. The method of using deep neural network to solve the automatically generated task of email subject line has been proposed recently. We experimentally explored the performance impact of multiple elements in this task. These experimental results will provide some guiding significance for the future research of this task. As far as we know, this is the first work to study and analyze the effects of related elements.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aliguliyev, R.M.: A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Expert Syst. Appl. 36(4), 7764–7772 (2009)
Bahgat, E.M., Rady, S., Gad, W., Moawad, I.F.: Efficient email classification approach based on semantic methods. Ain Shams Eng. J. 9(4), 3259–3269 (2018)
Barrios, F., López, F., Argerich, L., Wachenchauzer, R.: Variations of the similarity function of TextRank for automated summarization. In: Argentine Symposium on Artificial Intelligence (ASAI 2015)-JAIIO 44, (Rosario, 2015) (2015)
Carenini, G., Ng, R.T., Zhou, X.: Summarizing email conversations with clue words. In: Proceedings of the 16th International Conference on World Wide Web, pp. 91–100 (2007)
Carmel, D., Erera, S., Goldberg, I., Mizrachi, B.: Automatically generated subject recommendations for email messages based on email message content, US Patent 7,761,524, 20 July 2010
Carvalho, V.R., Cohen, W.W.: On the collective classification of email “speech acts”. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345–352 (2005)
Celikyilmaz, A., Bosselut, A., He, X., Choi, Y.: Deep communicating agents for abstractive summarization. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1662–1675 (2018)
Chang, M., Poon, C.K.: Using phrases as features in email classification. J. Syst. Softw. 82(6), 1036–1045 (2009)
Chen, Y.C., Bansal, M.: Fast abstractive summarization with reinforce-selected sentence rewriting. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 675–686 (2018)
Filippova, K., Alfonseca, E., Colmenares, C.A., Kaiser, Ł., Vinyals, O.: Sentence compression by deletion with LSTMs. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 360–368 (2015)
Lin, C.Y.: Looking for a few good metrics: automatic summarization evaluation-how many samples are enough? (2004)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)
Nallapati, R., Zhou, B., dos Santos, C.N., Gülçehre, Ç., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Goldberg, Y., Riezler, S. (eds.) Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, 11–12 August 2016, pp. 280–290. ACL (2016). https://doi.org/10.18653/v1/k16-1028
Shrestha, L., McKeown, K.: Detection of question-answer pairs in email conversations. In: Proceedings of the 20th International Conference on Computational Linguistics, p. 889. Association for Computational Linguistics (2004)
Sukhbaatar, S., Szlam, A., Fergus, R.: Learning multiagent communication with backpropagation. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 2252–2260 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Zajic, D.M., Dorr, B.J., Lin, J.: Single-document and multi-document summarization techniques for email threads using sentence compression. Inf. Process. Manag. 44(4), 1600–1610 (2008)
Zhang, R., Tetreault, J.: This email could save your life: introducing the task of email subject line generation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 446–456 (2019)
Zhong, M., Liu, P., Wang, D., Qiu, X., Huang, X.J.: Searching for effective neural extractive summarization: what works and what’s next. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1049–1058 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xue, M., Zhang, H., Lv, J. (2020). Key Factors of Email Subject Generation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_76
Download citation
DOI: https://doi.org/10.1007/978-3-030-63820-7_76
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63819-1
Online ISBN: 978-3-030-63820-7
eBook Packages: Computer ScienceComputer Science (R0)