文献类型： 会议论文

第一作者： Elena Callegari

作者： Elena Callegari ¹ ; Peter Vajdecka ² ; Desara Xhura ³ ; Anton Karl Ingason ¹ ;

作者机构： 1.University of Iceland Reykjavik, Iceland

2.Prague Univ. of Economics and Business Prague, Czechia

3.SageWrite ehf. Reykjavik, Iceland

会议名称： Workshop on Information Extraction from Scientific Publications

主办单位：

页码： 74-83

摘要： This study tackles the challenge of generating appropriate academic titles based on the paper's abstract. We approach this task as a high-level text summarization problem and introduce an innovative post-processing method that combines a predictive model with a set of linguistic rules to enhance the quality of the title generation. We start by evaluating three Natural Language Generation models (BART, T5, Flan T5), by identifying the top-performing model and by configuring it to generate diverse titles. We then conduct experiments employing various post-processing strategies -using SciBERT and linguistic rules- to select the best title out of all machine-generated options. Finally, we assess our title selection methods in relation to human evaluations.

分类号： tp391