Analytical study of Text Summarization Techniques

Summarization of Text is extracting important information from a body of text and present it in the form of a concise summary. The need for summarization has increased in recent times. The importance of having a simple concise summary of information in the news, business and research domains is paramount. Automatic text summarization is a well known task in the NLP (Natural Language Processing) field. Text Summarization techniques can be large divided into two groups: Extractive summarization and Abstractive summarization. Extractive summarization is based on identifying key sentences or phrases from the source text and grouping them to produce a summary without rewriting or paraphrasing the original text. Abstractive summarization is based on utilizing a deeper understanding of the source text and generating new sentences, not present in the original text, which improves the summary by reducing redundancy and focusing on the meaning of the source text. In this study we implement and compare the performance of various automatic summarization methods in order to gain insight into how long the methods take to implement and how accurate and human-like the generated summaries are. We aim to learn the pros and cons of the various techniques used by utilizing summary scoring as well as manual inspection of generated summary.