Abstract
Automatic text summarization is a very complex problem. Despite being intensively researched, automatic summaries are still considered to be of lower quality than manual summaries. This paper introduces a novel HCI approach to web page summarization. The proposed Crowd-Copy Summarizer follows the extractive text summarization approach of summarizing by selecting sentences within the text. The selection is performed by examining how frequently users copy certain sentences to their clipboards, for their own purposes. The most frequently copied sentences are included in the summary. Results from an early experiment are promising, as key sentences, such as introductory sentences, definitions, and important highlights, are copied frequently. Consequently, the generated summaries can provide good coverage of the main topics. This novel text summarization approach combines the best of both worlds: summarization based on collective human wisdom, without the expensive burden of manual summarization work.
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
Notes
References
Allahyari, M., et al.: Text summarization techniques: a brief survey. Int. J. Adv. Comput. Sci. Appl. 8(10) (2017). https://doi.org/10.14569/IJACSA.2017.081052
Kiani, F., Tas, O.: A survey on automatic text summarization. Press Start 5, 205–213 (2017). https://doi.org/10.17261/Pressacademia.2017.591. http://pressacademia.org/archives/pap/v5/29.pdf
Kirsh, I.: Automatic complex word identification using implicit feedback fromuser copy operations. In: Proceedings of the 21st International Conference on Web Information Systems Engineering (WISE 2020). Lecture Notes in Computer Science. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62008-0_11
Kirsh, I.: What web users copy to the clipboard on a website: a case study. In: Proceedings of the 16th International Conference on Web Information Systems and Technologies (WEBIST 2020). INSTICC, SciTePress, Setúbal, Portugal (2020, forthcoming)
Kirsh, I., Joy, M.: A different web analytics perspective through copy to clipboard heatmaps. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds.) ICWE 2020. LNCS, vol. 12128, pp. 543–546. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50578-3_41
Kirsh, I., Joy, M.: Splitting the web analytics atom: from page metrics and KPIs to sub-page metrics and KPIs. In: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020), Biarritz, France, pp. 33–43. Association for Computing Machinery, New York, June 2020. https://doi.org/10.1145/3405962.3405984
Rajasekaran, A., Varalakshmi, R.: Review on automatic text summarization. Int. J. Eng. Technol. (UAE) 7, 456–460 (2018). https://doi.org/10.14419/ijet.v7i2.33.14210
Saggion, H., Poibeau, T.: Automatic text summarization: past, present and future. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds.) Multi-Source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing, pp. 3–13. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-28569-1_1
Sajjan, R., Shinde, M.: A detail survey on automatic text summarization. Int. J. Comput. Sci. Eng. 7, 991–998 (2019). https://doi.org/10.26438/ijcse/v7i6.991998
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
Kirsh, I., Joy, M. (2020). An HCI Approach to Extractive Text Summarization: Selecting Key Sentences Based on User Copy Operations. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-60700-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-60700-5_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60699-2
Online ISBN: 978-3-030-60700-5
eBook Packages: Computer ScienceComputer Science (R0)