Summary of Cads: a Systematic Literature Review on the Challenges Of Abstractive Dialogue Summarization, by Frederic Kirstein et al.
CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization
by Frederic Kirstein, Jan Philip Wahle, Bela Gipp, Terry Ruas
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Transformers-based abstractive summarization for English dialogues is an area of ongoing research. This paper provides a comprehensive review of 1262 unique research papers published between 2019 and 2024, covering the main challenges in dialogue summarization, including language, structure, comprehension, speaker, salience, and factuality. The study links these challenges to corresponding techniques such as graph-based approaches, additional training tasks, and planning strategies, which typically rely on BART-based encoder-decoder models. While some challenges have seen significant progress due to training methods, others remain difficult and hold significant research opportunities. The paper also investigates how these approaches are typically assessed, covering established automatic metrics and human evaluation approaches for assessing scores and annotator agreement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews the current state of abstractive dialogue summarization using Transformers. Researchers have made progress in understanding language and structure, but challenges remain in comprehension, factuality, and salience. The study looks at how different techniques address these challenges, such as graph-based approaches and planning strategies. It also explores how researchers evaluate their models, using metrics like ROUGE and human evaluation. Overall, this paper provides a comprehensive look at the current state of dialogue summarization. |
Keywords
» Artificial intelligence » Encoder decoder » Rouge » Summarization
