Enhanced Neural Text Summarization With Syntactic And Headline Insights

Abstract

Text summarization plays a vital role in condensing large volumes of information into concise and coherent summaries. Traditional extractive methods often fail to capture semantic richness, while abstractive models face challenges in grammatical correctness and factual accuracy. The base paper introduced a syntax-augmented, headline-aware neural model that leverages syntactic features and headline guidance to improve summary generation. In this work, we extend this idea by evaluating both classical and modern approaches on the CNN/DailyMail dataset. First, we establish a baseline using the unsupervised Text Rank algorithm, which provides extractive summaries but achieves limited ROUGE scores. We then explore a state-of-the-art pretrained Transformer, DistilBART, which has been fine-tuned on CNN/DailyMail. Without additional training, DistilBART generates abstractive summaries with significantly higher accuracy, outperforming the base model on ROUGE-1 and ROUGE-2 metrics. Our experimental results show that incorporating headline cues and syntactic awareness (as in the base paper) improves traditional LSTM-based models, but modern pretrained Transformers demonstrate superior performance and efficiency. The findings highlight the evolution of summarization methods from handcrafted features to large-scale pretrained architectures, offering both practical insights for deployment and a strong foundation for future research.