Enhancing Academic Title Drafting Through Abstractive Summarization

Paper

Wu, Taoyu Wu and Shi, Kaize. 2024. "Enhancing Academic Title Drafting Through Abstractive Summarization." 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024). Harbin, China 16 - 18 Aug 2024 China. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/BESC64747.2024.10780612

Paper/Presentation Title	Enhancing Academic Title Drafting Through Abstractive Summarization
Presentation Type	Paper
Authors	Wu, Taoyu Wu and Shi, Kaize
Journal or Proceedings Title	Proceedings of the 2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024)
Number of Pages	7
Year	2024
Publisher	IEEE (Institute of Electrical and Electronics Engineers)
Place of Publication	China
ISBN	9798331531904
	9798331531911
Digital Object Identifier (DOI)	https://doi.org/10.1109/BESC64747.2024.10780612
Web Address (URL) of Paper	https://ieeexplore.ieee.org/abstract/document/10780612
Web Address (URL) of Conference Proceedings	https://ieeexplore.ieee.org/xpl/conhome/10779601/proceeding
Conference/Event	2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024)
Event Details	2024 IEEE International Conference on Behavioural and Social Computing (BESC-2024) Delivery In person Event Date 16 to end of 18 Aug 2024 Event Location Harbin, China
Abstract	The title of an academic paper encapsulates its core knowledge at a high level, while the abstract provides a concise summary of the paper's content. Therefore, an automatic title generation model utilizing abstractive summarization techniques can significantly aid researchers in drafting titles. This paper presents a model for generating titles for Chinese academic papers, leveraging the Transformer architecture and the pre-trained BERT model. Experimental results demonstrate that the Transformer model, augmented with pre-training, outperforms other models, achieving a Rouge-L score of 0.441. Human evaluations further indicate that the generated titles effectively support the decision-making process in academic title drafting.
Keywords	Automatic Text Summarization; Title Generation; LSTM Network; transformer; Seq2seq; BERT
Contains Sensitive Content	Does not contain sensitive content
ANZSRC Field of Research 2020	4602. Artificial intelligence
Public Notes	Files associated with this item cannot be displayed due to copyright restrictions.
Byline Affiliations	University of Ottawa, Canada
	University of Technology Sydney

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