Enhancing Extractive Text Summarization Through Ensemble Techniques

This chapter delves into the world of text summarization, focusing on how ensemble techniques can significantly enhance the process. It begins by highlighting the need for efficient summarization in today's fast-paced world, where digesting lengthy texts can be time-consuming. The study utilizes the BBC_Data dataset, which offers a diverse range of news articles across various sectors, to train and evaluate the summarization models. The chapter explores four base extractive summarization techniques: Latent Semantic Analysis (LSA), LexRank Summarizer, LUHN Summarizer, and SumBasic Summarizer. Each technique is thoroughly explained, including its working principles and how it contributes to the summarization process. The chapter also discusses the data processing steps involved in preparing the text for analysis. The results of the experimental analysis are presented, with each summarizer evaluated using ROUGE-1, ROUGE-2, and ROUGE-L scores. The ensemble approach, which combines the strengths of multiple summarizers, is shown to outperform individual techniques, offering a more comprehensive and accurate summary. The chapter concludes by discussing the future scope of text summarization, including potential improvements and features that could enhance the application's usability and accuracy. This detailed exploration provides a comprehensive overview of how ensemble techniques can revolutionize text summarization, making it an invaluable resource for professionals seeking to improve their text analysis capabilities.