21 citations · 82 across the 24 of their papers we have counts for
25 papers
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…
Overview of the SciHigh Track at FIRE 2025: Research Highlight Generation from Scientific Papers
Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay
`SciHigh: Research Highlight Generation from Scientific Papers' focuses on the task of automatically generating concise, informative, and meaningful bullet-point highlights directl…
TaxoAlign: Scholarly Taxonomy Generation Using Language Models
Avishek Lahiri, Yufang Hou, Debarshi Kumar Sanyal
Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive l…
S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Suman Adhya, Debarshi Kumar Sanyal
Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling. Vari…
DTECT: Dynamic Topic Explorer & Context Tracker
Suman Adhya, Debarshi Kumar Sanyal
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful…
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…
Evaluating Negative Sampling Approaches for Neural Topic Models
Suman Adhya, Avishek Lahiri, Debarshi Kumar Sanyal +1
Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of learn-to-compare. The goal…
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…
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…
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…
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…
GINopic: Topic Modeling with Graph Isomorphism Network
Suman Adhya, Debarshi Kumar Sanyal
Topic modeling is a widely used approach for analyzing and exploring large document collections. Recent research efforts have incorporated pre-trained contextualized language model…
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…
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…
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…
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…
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…
Improving Neural Topic Models with Wasserstein Knowledge Distillation
Suman Adhya, Debarshi Kumar Sanyal
Topic modeling is a dominant method for exploring document collections on the web and in digital libraries. Recent approaches to topic modeling use pretrained contextualized langua…
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…
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…
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…
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…
Personal Research Knowledge Graphs
Prantika Chakraborty, Sudakshina Dutta, Debarshi Kumar Sanyal
Maintaining research-related information in an organized manner can be challenging for a researcher. In this paper, we envision personal research knowledge graphs (PRKGs) as a mean…
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
Soumya Banerjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay +2
The abstract of a scientific paper distills the contents of the paper into a short paragraph. In the biomedical literature, it is customary to structure an abstract into discourse…