Summary of Recursive Abstractive Processing For Retrieval in Dynamic Datasets, by Charbel Chucri et al.
Recursive Abstractive Processing for Retrieval in Dynamic Datasets
by Charbel Chucri, Rami Azouz, Joachim Ott
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed algorithm efficiently maintains the recursive-abstractive tree structure in dynamic datasets without compromising performance. By applying query-focused recursive abstractive processing as a post-retrieval layer, this method overcomes limitations of other approaches. The paper demonstrates effectiveness through extensive experiments on real-world datasets, improving retrieval performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has led to the development of retrieval-augmented models that enhance basic methods by building hierarchical structures over retrieved text chunks. However, these models face challenges when dealing with dynamic datasets where adding or removing documents complicates updating hierarchical representations formed through clustering. To address this issue, a new algorithm is proposed to efficiently maintain recursive-abstractive tree structures in dynamic datasets without compromising performance. |
Keywords
» Artificial intelligence » Clustering
