DISSERTATION ABSTRACT

Studies In Aspects of Peer Review: Novelty,Scope, Research Lineage, Review Significance,and Peer Review Outcome
Tirthankar Ghosal
Department of Computer Science and Engineering
Indian Institute of Technology Patna, India
tirthankar.pcs16@iitp.ac.in
Abstract

The process of peer-review is considered as the sentinel of science in scholarly communications.
However, with the deluge of research articles and the overload of scholarly information, it is increasingly becoming difficult for humans to keep up with the pace of the latest research. This
thesis uses Machine Learning (ML) and Natural Language Processing (NLP) to investigate critical issues relevant to scholarly communications and peer review. Detecting whether a document
contains sufficient new information to be deemed as novel is of immense significance in this ageof data duplication. We investigate if we can automatically identify the newness in a document
based on its information content against a set of relevant documents. We create two benchmark
datasets: TAP-DLND 1.0 and TAP-DLND 2.0, and devise several methods for novelty detection
at the document level. One particular challenge in peer review is that the journal editors andconference program chairs are overwhelmed with the ever-increasing rise in article submissions.
Studies show that many submissions are not well-informed and do not fit within the scope of
the intended journal or conference. We investigate how an AI could assist the editors and program chairs in identifying potential out-of-scope submissions based on the past accepted papers of
the particular journal conference. We experiment with several methods, including feature-based
machine learning, deep multimodal architectures, to multiview clustering. Again, the recent unprecedented growth in paper submissions in major Machine Learning and AI conferences is a
grand challenge to the community to maintain high-quality reviewing practices while ensuring fairevaluation of the manuscripts. We investigate if we can identify which reviews were significant
enough to be considered in the decision-making. We design an end-to-end deep network thatleverages on exhaustiveness and strength of the peer review in a multi-task fashion to comprehend
it’s informativeness. Our DeepSentiPeer model is one of the first attempts to predict decisionsin peer review automatically. Finally, finding the relevant literature or identifying the works thathave inspired the current work under scrutiny is central to an evaluation in peer review. We
analyze the context of citations within paper full-text to identify a given research lineage. The
idea is to leverage citation significance classification to build a research lineage via a significant
citation graph. We argue that finding the lineage of given research would help identify the true
academic impact of a paper beyond quantitative citation counts.
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Awarded by: Indian Institute of Technology Patna, India on 28 August, 2021.
Supervised by: Asif Ekbal and Pushpak Bhattacharyya.
Available at: https://www.iitp.ac.in/~ai-nlp-ml/resources/dissertations/PhD/Tirthankar_Ghosal_PhD_Thesis_IIT_Patna.pdf.

Acknowledgement
Tirthankar Ghosal is a Visvesvesvaraya Ph.D. fellowship awardee (# VISPHD-MEITY-2518) andacknowledges Digital India Corporation, Ministry of Electronics and Information Technology,
Government of India, to support his doctoral studies.

Selected Publications
Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis,and Srinivasa Satya Sameer Kumar Chivukula. Novelty goes deep. a deep neural solution todocument level novelty detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2802–2813, Santa Fe, New Mexico, USA, August 2018. Associationfor Computational Linguistics. URL https://aclanthology.org/C18-1237.
Tirthankar Ghosal, Ashish Raj, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya. A
deep multimodal investigation to determine the appropriateness of scholarly submissions. InMaria Bonn, Dan Wu, J. Stephen Downie, and Alain Martaus, editors, 19th ACM/IEEE Joint
Conference on Digital Libraries, JCDL 2019, Champaign, IL, USA, June 2-6, 2019, pages 227–236. IEEE, 2019a. doi: 10.1109/JCDL.2019.00039. URL https://doi.org/10.1109/JCDL.
2019.00039.
Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, and Pushpak Bhattacharyya. DeepSentiPeer:Harnessing sentiment in review texts to recommend peer review decisions. In Proceedings of
the 57th Annual Meeting of the Association for Computational Linguistics, pages 1120–1130,
Florence, Italy, July 2019b. Association for Computational Linguistics. doi: 10.18653/v1/P19-1106. URL https://aclanthology.org/P19-1106.
Tirthankar Ghosal, Tanik Saikh, Tameesh Biswas, Asif Ekbal, and Pushpak Bhattacharyya.
Novelty Detection: A Perspective from Natural Language Processing. Computational Linguistics, pages 1–42, 12 2021a. ISSN 0891-2017. doi: 10.1162/coli a 00429. URL https://doi.org/10.1162/coli_a_00429.
Tirthankar Ghosal, Piyush Tiwary, Robert Patton, and Christopher Stahl. Towards establishinga research lineage via identification of significant citations. Quantitative Science Studies, pages
1–19, 2021b. URL https://doi.org/10.1162/qss_a_00170.

ACM SIGIR Forum

2Vol. 55 No. 2 December 2021