Search Results for author:
Found 15 papers, 5 papers with code
Evaluating LLMs and Pre-trained Models for Text Summarization Across Diverse Datasets
Text summarization plays a crucial role in natural language processing by condensing large volumes of text into concise and coherent summaries.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning
Artificial intelligence systems significantly impact the environment, particularly in natural language processing (NLP) tasks.
Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation of CNNs
With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs).
Can pre-trained language models generate titles for research papers?
In this paper, we fine-tune pre-trained language models to generate titles of papers from their abstracts.
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis
Financial sentiment analysis allows financial institutions like Banks and Insurance Companies to better manage the credit scoring of their customers in a better way.
Analysis of Multidomain Abstractive Summarization Using Salience Allocation
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.
Automatic Recognition of Learning Resource Category in a Digital Library
Digital libraries often face the challenge of processing a large volume of diverse document types.
document-image-classification
Document Image Classification
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Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023
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.
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis
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.
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis
The success of self-supervised learning (SSL) has mostly been attributed to the availability of unlabeled yet large-scale datasets.
Abstractive Text Summarization using Attentive GRU based Encoder-Decoder
Indeed automatic text summarization has emerged as an important application of machine learning in text processing.
An Analysis of Abstractive Text Summarization Using Pre-trained Models
People nowadays use search engines like Google, Yahoo, and Bing to find information on the Internet.
Named Entity Recognition Based Automatic Generation of Research Highlights
We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights.
Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings
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.
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
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.
