From Extractive to Abstractive Summarization: A Journey

Dr. Parth Mehta completed his M.Tech. in Machine Intelligence and his Ph.D. in Text Summarization at Dhirubhai Ambani Institute of ICT (DA-IICT), Gandhinagar, India. At the DA-IICT he was part of the Information Retrieval and Natural Language Processing Lab. He was also involved in the national project “Cross Lingual Information Access”, funded by the Govt. of India, which focused on building a cross-lingual search engine for nine Indian languages. Dr. Mehta has served as reviewer for the journals Information Processing and Management and Forum for Information Retrieval Evaluation. Apart from several journal and conference papers, he has also co-edited a book on text processing published by Springer. Prof. Prasenjit Majumder is an Associate Professor at Dhirubhai Ambani Institute of ICT (DA-IICT), Gandhinagar and a Visiting Professor at the Indian Institute of Information Technology, Vadodara (IIIT-V). Prof. Majumder completed his Ph.D. at JadavpurUniversity in 2008 and worked as a postdoctoral fellow at the University College Dublin, prior to joining the DA-IICT, where he currently heads the Information Retrieval and Language Processing Lab. His research interests lie at the intersection of Information Retrieval, Cognitive Science and Human Computing Interaction. He has headed several projects sponsored by the Govt. of India. He is one of the pioneers of the Forum for Information Retrieval Evaluation (FIRE), which assesses research on Information Retrieval and related areas for South Asian languages. Since being founded in 2008, FIRE has grown to become a respected conference, drawing participants from across the globe. Prof. Majumder has authored several journal and conference papers, and co-edited two special issues of Transactions in Information Systems (ACM). He has co-edited two books: ‘Multi Lingual Information Access in South Asian Languages’ and ‘Text Processing,’ both published by Springer.

• Introduction.-Related Work.-  Corpora and Evaluation for Text Summarization.- Domain Specific Summarization.- Improving sentence extraction through rank aggregation.- Leveraging content similarity in summaries for generating better ensembles.-Neural model for sentence compression.-  Conclusion.