Assessing Abstractive and Extractive Methods for Automatic News Summarization

Automatic Text Summarization (ATS) is a research area that originated in the late 1950s and has gained increasing importance with the surge of text data available today. ATS approaches are generally classified into extractive and abstractive methods. Extractive summarization selects the most relevant sentences from a text and copies them verbatim into the summary. Abstractive text summarization aims to generate a more cohesive and concise version of the original text. Recently, pre-trained and large language models such as BERT, BART, Pegasus, Llama, Gemma, and GPT-3 have revolutionized numerous natural language tasks, including creating more humanlike summaries. Despite the progress in recent years, there is room for improvement in directly comparing different summarization tools using a corpus containing high-quality reference summaries. This paper evaluates the quality of summaries generated by such tools, comparing the results obtained from abstractive and extractive summarization methods. Experiments were conducted using the CNN-corpus, focusing on news articles written in English and employing four evaluation measures. The experimental results unveiled that the Pegasus model outperformed others in abstractive summarization. These findings highlight the potential for further advancements in the field, suggesting that using more specialized models explicitly tailored for the summarization task may yield superior results compared to large general-purpose language models.