Summary of Jads: a Framework For Self-supervised Joint Aspect Discovery and Summarization, by Xiaobo Guo et al.
JADS: A Framework for Self-supervised Joint Aspect Discovery and Summarization
by Xiaobo Guo, Jay Desai, Srinivasan H. Sengamedu
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The paper proposes a novel approach to generate summaries that incorporate multiple aspects or topics in text documents. The current methods use clustering or topic modeling to group relevant sentences and then generate a summary for each group, but these approaches struggle to optimize the summarization and clustering algorithms jointly. Instead, the authors introduce the Joint Aspect Discovery and Summarization (JADS) algorithm, which integrates topic discovery and summarization into a single step. The JADS model outperforms two-step baselines and achieves better performance and stability with pretraining. Additionally, the embeddings derived from JADS exhibit superior clustering capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating summaries that cover multiple topics or ideas in text documents. Right now, most methods group relevant sentences together and then create a summary for each group, but these approaches don’t do well when it comes to optimizing both summarization and grouping at the same time. The authors suggest a new approach called Joint Aspect Discovery and Summarization (JADS), which does both topic discovery and summarization in one step. This new method performs better than old methods and gets even better with some extra training. Plus, the results from this method are good for clustering similar ideas together. |
Keywords
» Artificial intelligence » Clustering » Pretraining » Summarization
