المفاهيم الأساسية
This survey explores process-oriented automatic text summarization and the impact of Large Language Models (LLMs) on ATS methods.
الملخص
This comprehensive survey delves into the evolution of Automatic Text Summarization (ATS) methods, emphasizing practical implementations and the influence of Large Language Models (LLMs). The study covers various approaches, from statistical models to deep learning techniques, providing insights into the challenges and advancements in the field.
الإحصائيات
"The dataset contains nearly 10 million English news documents and summaries are made up of news headlines."
"The XSum dataset contains 226,711 Wayback archived BBC articles ranging over almost a decade (2010 to 2017) and covering a wide variety of domains."
"Scisumm contains the 1,000 most cited academic papers in the ACL Anthology Network."
"ArXiv, PubMed datasets contain more than 300,000 academic papers in total."
"WikiHow dataset consists of more than 230,000 article-summary pairs obtained from WikiHow knowledge base."
"LCSTS consists of over 2 million real Chinese short blogs from various domains."
اقتباسات
"Automatic Text Summarization aims to condense extensive texts into concise and accurate summaries using NLP algorithms."
"Large Language Models have significantly improved the accuracy and coherence of generated summaries."
"The emergence of deep learning models has steered the trajectory of ATS towards advanced modeling techniques."
"Pre-training based approaches have substantially elevated the performance of ATS tasks."
"Extractive summarization models demonstrate an enhanced capability to capture precise terminologies."