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Found 11 papers, 3 papers with code
Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports
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.
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.
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.
Hallucination Reduction in Long Input Text Summarization
We have used the following metrics to measure factual consistency at the entity level: precision-source, and F1-target.
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.
