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20232026
most citedAn Analysis of Abstractive Text Summarization Using Pre-trained Models

21 citations · 57 across the 13 of their papers we have counts for

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cs.CL2026

Automatic Generation of Titles for Research Papers Using Language Models

Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay

The title of a research paper conveys its primary idea and, occasionally, its conclusions in a clear and concise manner. Choosing an appropriate title is often challenging, and aut…

cs.CL2025

Enhancing Business Analytics through Hybrid Summarization of Financial Reports

Tohida Rehman

Financial reports and earnings communications contain large volumes of structured and semi structured information, making detailed manual analysis inefficient. Earnings conference…

cs.CL2025

Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports

Anindita Bhattacharya, Tohida Rehman, Debarshi Kumar Sanyal +1

The findings section of a radiology report is often detailed and lengthy, whereas the impression section is comparatively more compact and captures key diagnostic conclusions. This…

cs.CL2025

Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets

Tohida Rehman, Soumabha Ghosh, Kuntal Das +3

Text summarization plays a crucial role in natural language processing by condensing large volumes of text into concise and coherent summaries. As digital content continues to grow…

cs.CL2025

How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning

Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay

Artificial intelligence systems significantly impact the environment, particularly in natural language processing (NLP) tasks. These tasks often require extensive computational res…

cs.CL2024★ 2 cited

Can pre-trained language models generate titles for research papers?

Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay

The title of a research paper communicates in a succinct style the main theme and, sometimes, the findings of the paper. Coming up with the right title is often an arduous task, an…

cs.CL2024★ 2 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.CL2024

Analysis of Multidomain Abstractive Summarization Using Salience Allocation

Tohida Rehman, Raghubir Bose, Soumik Dey +1

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 de…

cs.CL2023★ 1 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.CL2023★ 21 cited

An Analysis of Abstractive Text Summarization Using Pre-trained Models

Tohida Rehman, Suchandan Das, Debarshi Kumar Sanyal +1

People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet. Due to explosion in data, it is helpful for users if they are provided relevant…

cs.CL2023★ 2 cited

Named Entity Recognition Based Automatic Generation of Research Highlights

Tohida Rehman, Debarshi Kumar Sanyal, Prasenjit Majumder +1

A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a…

cs.CL2023★ 9 cited

Abstractive Text Summarization using Attentive GRU based Encoder-Decoder

Tohida Rehman, Suchandan Das, Debarshi Kumar Sanyal +1

In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of…

cs.CL2023★ 20 cited

Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings

Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay +2

Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identi…