Summary of Product Description and Qa Assisted Self-supervised Opinion Summarization, by Tejpalsingh Siledar et al.
Product Description and QA Assisted Self-Supervised Opinion Summarization
by Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Sri Raghava Ravindra Muddu, Suman Banerjee, Amey Patil, Sudhanshu Shekhar Singh, Muthusamy Chelliah, Nikesh Garera, Swaprava Nath, Pushpak Bhattacharyya
First submitted to arxiv on: 8 Apr 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 The proposed novel synthetic dataset creation (SDC) strategy leverages information from reviews as well as product descriptions and question-answers (QA) for selecting one of the reviews as a pseudo-summary, enabling supervised training. The Multi-Encoder Decoder framework for Opinion Summarization (MEDOS) employs separate encoders for each source, allowing effective selection of information while generating summaries. The combination of SDC approach and MEDOS model achieves on average a 14.5% improvement in ROUGE-1 F1 over the state-of-the-art (SOTA). Comparative analysis highlights the significance of incorporating additional sources for generating more informative summaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to summarize opinions about products by combining information from different sources like reviews, product descriptions, and questions. This approach helps train a special kind of artificial intelligence that can generate better summaries. The results show that this approach is much better than existing methods, with an average improvement of 14.5%. It’s also important because it allows the AI to learn from more data and create more informative summaries. |
Keywords
» Artificial intelligence » Encoder decoder » Rouge » Summarization » Supervised
