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

21 citations · 35 across the 6 of their papers we have counts for

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6 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.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.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.CL202321 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.CL20232 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.CL20239 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…