SciRelate: A Framework for Related Work Generation with TopicFusion-MP for Subsection Heading Extraction

Writing the Related Work section is a challenging task for inexperienced researchers, requiring significant time, effort, and skill. This study aims to ease this burden by introducing an ICT-based framework that automates the generation of cohesive and well-structured Related Work sections. We propose SciRelate, a novel framework that combines abstractive multidocument summarization and automated keyword-based subsection heading extraction. SciRelate comprises three main modules: (1) document clustering using Latent Dirichlet Allocation (LDA), (2) related Work generation via a fine-tuned Bidirectional and Autoregressive Transformer (BART) model, and (3) Topic keyword extraction using TopicFusion-MP, a hybrid method that integrates LDA, TextRank, and MPNET embeddings. Experiments were conducted using the NudtRwG dataset, and BART was evaluated using 10-fold cross-validation. LDA clustering effectively identified subsection headings aligned with ground truth structures. The BART model achieved high ROUGE scores: 81.53 (ROUGE-1), 76.19 (ROUGE-2), and 79.03 (ROUGE-L). TopicFusion-MP outperformed six baseline keyword extraction methods in four scenarios, reaching a top similarity score of 0.6398. This study presents a comprehensive, automated pipeline for generating Related Work sections, integrating clustering, summarization, and keyword extraction in a unified framework. SciRelate highlights the potential of ICT tools to support scientific writing, particularly for new researchers.