Summary of Beyond Extraction: Contextualising Tabular Data For Efficient Summarisation by Language Models, By Uday Allu et al.
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
by Uday Allu, Biddwan Ahmed, Vishesh Tripathi
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed approach enhances the accuracy of complex table queries in Retrieval-Augmented Generation (RAG) systems by introducing an innovative methodology that combines PDF storage, tabular content extraction, context enrichment, and language model summarization. The method uses fine-tuned Llama-2-chat models for summary generation and ChatGPT 3.5 API for contextual sense augmentation. This enriched data is then integrated into the retrieval database, aiming to significantly improve the precision of complex table queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to help computers understand tables in documents better. It’s like trying to find specific information within a huge library. The team found that by storing documents and extracting tables separately, they could make it easier for computers to understand what’s inside those tables. They used special language models to summarize the table contents and even added more context using ChatGPT. This new approach can help computers find specific information in tables much more accurately. |
Keywords
* Artificial intelligence * Language model * Llama * Precision * Rag * Retrieval augmented generation * Summarization
