Rouge Metric Evaluation for Text Summarization Techniques

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Rouge Metric Evaluation for Text Summarization Techniques 31 Pages Posted: 26 May 2022 See all articles by Marcello BarbellaMarcello Barbellaaffiliation not provided to SSRNGenoveffa Tortoraaffiliation not provided to SSRN Abstract Approaches to Automatic Text Summarization try to extract key information from one or more input texts and generate summaries whilst preserving content meaning. These strategies are separated into two groups: Extractive and Abstractive, which differ in terms of how they work. The former extracts sentences from the document text directly, whereas the latter creates a summary by interpreting the text and rewriting sentences often with new words. So, it is important to assess and confirm how similar a summary is to the original text. The question is: how can the quality of these methodologies of summaries be evaluated? For the evaluation of text summarization results, various metrics and scores have been proposed in the literature, but the most used is ROUGE.Then, the primary goal of this study is to accurately estimate the ROUGE metric's behavior. We conducted a first experiment to compare the metric efficiency for evaluating Abstractive versus Extractive Text Summarization algorithms, and a second one to compare the obtained score for two different summary approaches: a simple execution of a summarization algorithm versus a multiple execution of different algorithms on the same text. Our results have shown that: ROUGE does not obtain impressive results since it behaves in a similar way both on the Abstractive and Extractive algorithms; furthermore, in most cases, a multiple execution is better than a single one. Keywords: Automatic Text Summarization Algorithms, Extractive, Abstractive, ROUGE metric, Bert Suggested Citation: Suggested Citation Barbella, Marcello and Tortora, Genoveffa, Rouge Metric Evaluation for Text Summarization Techniques. Available at SSRN: https://ssrn.com/abstract=4120317 Marcello Barbella (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Genoveffa Tortora affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you want regular updates from SSRN on Twitter? Follow SSRN Paper statistics Downloads 1 Abstract Views 5 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.262 seconds