An Efficient Model for Academic Paper Title Generation using Summarization Approach

The title should concisely encapsulate the overall content; however, producing a strong academic title that reflects the core contribution remains challenging. This paper proposes and evaluates a pipeline-based model for academic paper title generation: abstracts collected from the NIPS dataset are preprocessed, condensed via extractive summarization, and then Standard YAKE is applied to extract weighted keywords. Titles are generated from a fixed Top-6 keyword budget while preserving the YAKE ranking order; in this work, the proposed model refers to the end-to-end pipeline configuration rather than a newly trained neural architecture. We compare four summarization algorithms (Luhn, LSA, Edmundson, and KL) based on their YAKE-weighted keywords, and adopt Luhn as default because it produces more topic-relevant YAKE-weighted keywords than the other summarizers. We illustrate the pipeline by using three qualitative examples and compare three generator-Ateeqq/keywords-title-generator, KeyToText (k2t), and GPT-2 (gpt2)-under identical Top-6 constraints. Experiments on 300 papers using both lexical and semantic similarity metrics (ROUGE-L, TF-IDF cosine similarity, BERTScore-F1, and SciBERTScore-F1) indicate that the adopted Luhn→YAKE→Top-6→LLM pipeline produces the most semantically aligned titles under identical input constraints.