ContriSci: A BERT-Based Multitasking Deep Neural Architecture to Identify Contribution Statements from Research Papers

Komal Gupta, Ammaar Ahmad, Tirthankar Ghosal, Asif Ekbal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

With the rapid growth of scientific literature, it is becoming increasingly difficult to identify scientific contribution from the deluge of research papers. Automatically identifying the specific contribution made in a research paper would help quicker comprehension of the work, faster literature survey, comparison with the related works, etc. Here in this work, we investigate methods to automatically extract the contribution statements from research articles. We design a multitask deep neural network leveraging section identification and citance classification of scientific statements to predict whether a given scientific statement specifies a contribution or not. In the long-run, we envisage to create a knowledge graph of scientific contributions for machine comprehension and more straightforward navigation of research contributions in a particular domain. Our approach achieves the best performance over earlier methods (a relative improvement of 8.08% in terms of F1 score) for contributing sentence identification over a dataset of Natural Language Processing (NLP) papers. We make our code available at here (https://github.com/ammaarahmad1999/Sem-Eval-2021-Task-A ).

Original languageEnglish
Title of host publicationTowards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings
EditorsHao-Ren Ke, Chei Sian Lee, Kazunari Sugiyama
PublisherSpringer Science and Business Media Deutschland GmbH
Pages436-452
Number of pages17
ISBN (Print)9783030916688
DOIs
StatePublished - 2021
Externally publishedYes
Event23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021 - Virtual, Online
Duration: Dec 1 2021Dec 3 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13133 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021
CityVirtual, Online
Period12/1/2112/3/21

Funding

Acknowledgement. Asif Ekbal is a recipient of the Visvesvaraya Young Faculty Award and acknowledges Digital India Corporation, Ministry of Electronics and Information Technology, Government of India for supporting this research.

Keywords

  • Contribution identification
  • Information extraction
  • Multitask learning

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