no code implementations • 19 Feb 2024 • Tohida Rehman, Raghubir Bose, Soumik Dey, Samiran Chattopadhyay
This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques.
no code implementations • 28 Nov 2023 • Soumya Banerjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay, Plaban Kumar Bhowmick, Partha Pratim Das
Digital libraries often face the challenge of processing a large volume of diverse document types.
Document Image Classification Optical Character Recognition (OCR)
no code implementations • 27 Oct 2023 • Srijoni Majumdar, Soumen Paul, Debjyoti Paul, Ayan Bandyopadhyay, Samiran Chattopadhyay, Partha Pratim Das, Paul D Clough, Prasenjit Majumder
The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels.
no code implementations • 9 Mar 2023 • Soumitri Chattopadhyay, Soham Ganguly, Sreejit Chaudhury, Sayan Nag, Samiran Chattopadhyay
In this paper, we seek to tackle these concerns head-on and systematically explore the applicability of non-contrastive self-supervised learning (SSL) algorithms under federated learning (FL) simulations for medical image analysis.
1 code implementation • 3 Mar 2023 • Soumitri Chattopadhyay, Soham Ganguly, Sreejit Chaudhury, Sayan Nag, Samiran Chattopadhyay
The success of self-supervised learning (SSL) has mostly been attributed to the availability of unlabeled yet large-scale datasets.
no code implementations • 25 Feb 2023 • Tohida Rehman, Suchandan Das, Debarshi Kumar Sanyal, Samiran Chattopadhyay
People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet.
no code implementations • sdp (COLING) 2022 • Tohida Rehman, Debarshi Kumar Sanyal, Prasenjit Majumder, Samiran Chattopadhyay
We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights.
no code implementations • 25 Feb 2023 • Tohida Rehman, Suchandan Das, Debarshi Kumar Sanyal, Samiran Chattopadhyay
Indeed automatic text summarization has emerged as an important application of machine learning in text processing.
1 code implementation • 14 Feb 2023 • Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay, Plaban Kumar Bhowmick, Partha Pratim Das
On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31. 78, 9. 76 and 29. 3, respectively, METEOR score of 24. 00, and BERTScore F1 of 85. 25.
1 code implementation • 11 May 2020 • Soumya Banerjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay, Plaban Kumar Bhowmick, Parthapratim Das
In the biomedical literature, it is customary to structure an abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT, and CONCLUSION, but this segmentation is uncommon in other fields like computer science.