Novelties in text summarization using state of the art natural language processing techniques
Swagata Ashwani
Carnegie Mellon University | USA
: J Comput Eng Inf Technol
Abstract
Over the past decade, the internet has stormed and given rise to an enormous amount of data. The more data we have, the less information is getting accessible, thus, the need for a solution emerges, that transforms this vast raw information into useful information which a human brain can understand has become imperative. Text summarization is one such technique that can resolve this issue. Automatic summarization is an approach which is used to reduce a document to its main ideas. It operates by preserving substantial information by creating a shortened version of the text. There are two categories of Text Summarization - Extractive and Abstractive methods. Extractive methods of summarization minimize the burden of summarization by choosing from the actual text a subset of sentences that are relevant. The workshop talks about the state of the art extractive methods that can be used to perform text summarization.
Recent Publications :
1. Tariq Yousef and Stefan Jänicke. 2021. A survey of text alignment visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2):1149– 1159.
2. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
3. Zheng Zhao, Shay B. Cohen, and Bonnie Webber. 2020. Reducing quantity hallucinations in abstractive summarization. In Findings of the Association for Computational Linguistics EMNLP 2020, Online. Association for Computational Linguistics.
Biography
Swagata has completed her Masters from Carnegie Mellon University, USA. She has over 6 years of experience in Data Science and is currently leading the Data Science initiatives at Boomi. She is an avid blogger and actively participates in forums such as Women in Tech, and Data Science initiatives. In her spare time, she loves to travel and working out.
