Abstract: Title generation,as a fundamental component of text generation tasks,frequently encounters challenges such as inadequate information coverage and semantic deviation.To address this issue,this paper proposes a multi-task title generation model guided by core sentences.This model emphasizes the critical role of core sentences in capturing the main idea of the source text and improving title generation quality.The model utilizes the original text,core sentences,and keywords as inputs,employing annotated core sentences during the training phase and acquiring them automatically through a core sentence classification task du-ring the testing phase.By integrating the training of core sentence classification and title generation,the model is capable of identi-fying key content while generating titles that more accurately align with the semantic meaning of the source text.Furthermore,to enhance the quality of generation,a similarity loss between keywords and titles is introduced to reinforce thematic consistency.During the decoding phase,the model explicitly distinguishes between two cognitive processes－content understanding and conceptual focus－in an educational context.It employs a dual cross-attention mechanism to generate concise,fluent,and highly summarized titles.Experimental results demonstrate that,within the multi-task framework,the outcomes of the core sentence classification task assist the title generation task.The presence of shared information enables collaborative optimization between tasks,leading to a substantial enhancement in title generation quality and providing novel insights for the automated construction of educational resources.

Key words: Title generation, Core sentences, Keywords, Multitask learning, Abstractive summarization