most citedAn Analysis of Abstractive Text Summarization Using Pre-trained Models

21 citations · 41 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CL20242 cited

Transfer Learning and Transformer Architecture for Financial Sentiment Analysis

Tohida Rehman, Raghubir Bose, Samiran Chattopadhyay +1

Financial sentiment analysis allows financial institutions like Banks and Insurance Companies to better manage the credit scoring of their customers in a better way. Financial doma…

cs.DL2024

Automatic Recognition of Learning Resource Category in a Digital Library

Soumya Banerjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay +2

Digital libraries often face the challenge of processing a large volume of diverse document types. The manual collection and tagging of metadata can be a time-consuming and error-p…

cs.CL20231 cited

Hallucination Reduction in Long Input Text Summarization

Tohida Rehman, Ronit Mandal, Abhishek Agarwal +1

Hallucination in text summarization refers to the phenomenon where the model generates information that is not supported by the input source document. Hallucination poses significa…

cs.CL20231 cited

CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers

Avishek Lahiri, Debarshi Kumar Sanyal, Imon Mukherjee

Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove be…

cs.CL20232 cited

What Does the Indian Parliament Discuss? An Exploratory Analysis of the Question Hour in the Lok Sabha

Suman Adhya, Debarshi Kumar Sanyal

The TCPD-IPD dataset is a collection of questions and answers discussed in the Lower House of the Parliament of India during the Question Hour between 1999 and 2019. Although it is…

cs.CL2023

Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling

Suman Adhya, Avishek Lahiri, Debarshi Kumar Sanyal

Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-ou…