Summary of Distilling Opinions at Scale: Incremental Opinion Summarization Using Xl-opsumm, by Sri Raghava Muddu et al.
Distilling Opinions at Scale: Incremental Opinion Summarization using XL-OPSUMM
by Sri Raghava Muddu, Rupasai Rangaraju, Tejpalsingh Siledar, Swaroop Nath, Pushpak Bhattacharyya, Swaprava Nath, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Sudhanshu Shekhar Singh, Nikesh Garera
First submitted to arxiv on: 16 Jun 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 Xl-OpSumm framework is a scalable solution for opinion summarization in e-commerce, capable of handling large volumes of reviews. By leveraging Large Language Models (LLMs) such as GPT-4 and Llama-3-8B-8k, the framework generates summaries incrementally, improving upon existing models like AMASUM. The authors evaluate Xl-OpSumm on two datasets, AMASUM and Xl-Flipkart, achieving significant ROUGE-1 F1 gains of 4.38% and ROUGE-L F1 gains of 3.70% compared to the next best-performing model. This paper demonstrates the potential for LLMs in real-world applications like e-commerce, highlighting the importance of scalable summarization techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make sense of people’s opinions about products on online shopping websites. Imagine having thousands of reviews about a product, each one saying something different. It can be hard to understand what most people are really saying. Scientists created a new way to summarize these opinions called Xl-OpSumm. This framework uses special computer models to group similar thoughts together and create a shorter summary. The team tested their method on two sets of reviews and found that it works much better than other methods. |
Keywords
» Artificial intelligence » Gpt » Llama » Rouge » Summarization
