Automatic Text Summarization using Wu-Palmer Measure and Graph based Sentence Selection Rishav Rakshit 1 , Chandralika Chakraborty 2 , Udit Kr. Chakraborty 3 and Bhairab Sarma 4 1 Microsoft Corporation, Hyderabad, India Email: rishav.rakshit@microsoft.com 2-3 Sikkim Manipal Institute of Technology, (SMU), Majhitar, India Email: {chandralika.c, udit.c }@smit.smu.edu.in 4 University of Science & Technology Meghalaya, Baridua, India Email: sarmabhairab@gmail.com Abstract—In this paper, an extraction based text summarization methodology is proposed that aims to overcome issues faced with traditional extraction based summarizers using a combination of testing semantic similarity using the Wu-Palmer Measure and a graph-based sentence selection scheme for single document summarizations. The proposed approach is not only independent of context, syntax and language but also promotes shorter summaries via its sentence selection scheme. This method has been tested using the standard Reuters-21578 collection against widely used TextRank based summarizers and has shown promising results for single-document summarizations. Index Terms— Automatic, Text Summarization, Wu Palmer, Graph, extraction. I. INTRODUCTION The present decade is witnessing amelioration of connectivity. The massive reach of the internet and its rate of growth has increased web-based content manifold. This has necessitated the development of text summarizers which can be used to retrieve relevant information out of large texts. Text summarizers capable of delivering succinct content, retaining the originality of the source document find large scale applications in the fields of finance [1], journalism [2], medicine [3], internet search engines and even generic research [4]. A summary, according to Radev et.al [5] is defined as “a text that is produced from one or more texts, that conveys important information in the original text(s), and that is no longer than half of the original text(s) and usually, significantly less than that.” Automatic text summarization therefore can be taken as the process of automatically producing the summary from a given text. There exists two approaches to automatic text summarization, namely extractive summarization and abstractive summarization. Extractive summaries consist of important sentences extracted from the parent text and copied verbatim into the summary. Abstractive summaries, in contrast try to generate fresh sentences conveying the most critical information from the text to be summarized. Traditional extractive summarizers do not guarantee a coherent narration [6] but can represent an approximation of the content of the text with relative ease. The current paper proposes a method of extractive summarization of single documents. The method proposed, Grenze ID: 01.GIJET.8.1.521 © Grenze Scientific Society, 2022 Grenze International Journal of Engineering and Technology, Jan Issue