Summary of Incremental Extractive Opinion Summarization Using Cover Trees, by Somnath Basu Roy Chowdhury et al.
Incremental Extractive Opinion Summarization Using Cover Trees
by Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi
First submitted to arxiv on: 16 Jan 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 In this paper, researchers tackle the challenge of updating opinion summaries for products or services as user reviews accumulate over time. Extractive opinion summarization involves selecting representative sentences that capture prevalent opinions in a review set. The authors focus on the incremental setting, where reviews arrive one at a time, and propose an efficient algorithm called CoverSumm to compute CentroidRank summaries. This approach relies on indexing review representations in a cover tree and maintaining a reservoir of candidate summary review sentences. The results show that CoverSumm is up to 36x faster than baseline methods and capable of adapting to nuanced changes in data distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make online shopping easier by updating opinion summaries for products as users leave reviews. Researchers want to know how to do this efficiently, especially when new reviews come in over time. They looked at a way called CentroidRank that picks out important sentences from reviews and found it’s not good enough for real-time updates. Instead, they created an algorithm called CoverSumm that uses special data structures to make the process much faster and more accurate. |
Keywords
* Artificial intelligence * Summarization
