Summary of Abstractive Text Summarization: State Of the Art, Challenges, and Improvements, by Hassan Shakil et al.
Abstractive Text Summarization: State of the Art, Challenges, and Improvements
by Hassan Shakil, Ahmad Farooq, Jugal Kalita
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 survey paper provides a comprehensive overview of abstractive text summarization techniques, categorizing state-of-the-art methods into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. The review delves into prevailing challenges, such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper also highlights emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. By providing a structured overview of the domain, this paper aims to enable researchers and practitioners to better understand the current landscape and identify potential areas for further research and improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about summarizing text in a way that keeps the main ideas but changes some details. It looks at what are called “abstractive” techniques, which are different from others because they create new text instead of just copying parts from the original. The paper talks about the challenges and problems with these techniques, like making sure the summary makes sense and doesn’t have errors. It also suggests ways to solve these problems and mentions areas where more research is needed, such as summarizing documents that are long or in different languages. |
Keywords
» Artificial intelligence » Multi modal » Reinforcement learning » Summarization
