Summary of A Comprehensive Survey on Process-oriented Automatic Text Summarization with Exploration Of Llm-based Methods, by Hanlei Jin et al.
A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods
by Hanlei Jin, Yang Zhang, Dan Meng, Jun Wang, Jinghua Tan
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive overview of Automatic Text Summarization (ATS) from a “Process-Oriented Schema” perspective, which enables practical real-world implementations. The authors aim to bridge the two-year gap in the literature by reviewing both traditional and Large Language Model-based (LLM-based) ATS methods. Specifically, they provide an update on LLM-based ATS works and survey recent advances in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ATS aims to create concise summaries using Natural Language Processing algorithms, reducing human effort required for processing large text volumes. While many studies have surveyed ATS methods, they often lack practicality for real-world implementations. This paper provides a comprehensive overview of ATS, including LLM-based approaches, making it a valuable resource for researchers and practitioners. |
Keywords
» Artificial intelligence » Large language model » Natural language processing » Summarization
