Abstract: The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence ...

Abstract: This research develops a supervised learning framework for improved text summarization in Natural Language Processing (NLP) systems, including the aspects of text relevance, coherence and ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

Discover seven underrated Gemini prompts that go beyond the basics — from bookshelf analysis to stress-free trip planning and ...

Summarization of texts have been considered as essential practice nowadays with the careful presentation of the main ideas of a text. The current study aims to provide a methodology of summarizing ...

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Introduction: Text summarization is a longstanding challenge in natural language processing, with recent advancements driven by the adoption of Large Language Models (LLMs) and Small Language Models ...

New claims for unemployment benefits rose more than expected last week, signaling growing weakness in the labor market. But if the job market is weak, why are people getting all those texts offering ...

Objective: This study aims to present the current state of the art on clinical text summarization using large language models, evaluate the level of evidence in existing research and assess the ...

Gallagher, S., Rallapalli, S., and Brooks, T., 2025: Evaluating LLMs for Text Summarization: An Introduction. Carnegie Mellon University, Software Engineering ...