Text Summarization
ActivePI: Nisansa de Silva
We propose to advance automatic summarization and generation techniques, with a focus on aspect-aware methods that capture fine-grained information and sentiment within concise textual representations.
@inproceedings{kumarasinghe2022automatic,
title={{Automatic Generation of Abstracts for Research Papers}},
author={Kumarasinghe, Dushan and de Silva, Nisansa},
booktitle={Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)},
pages={221--229},
year={2022},
organization={ACL},
abstract={Summarizing has always been an important utility for reading long documents. Research papers are unique in this regard, as they have a compulsory summary in the form of the abstract in the beginning of the document which gives the gist of the entire study often within a set upper limit for the word count. Writing the abstract to be sufficiently succinct while being descriptive enough is a hard task even for native English speakers. This study is the first step in generating abstracts for research papers in the computational linguistics domain automatically using the domain-specific abstractive summarization power of the GPT-Neo model.},
misc={https://aclanthology.org/2022.rocling-1.28/,NLP,https://goo.gl/iY6aTr}
}