Summary of Qfmts: Generating Query-focused Summaries Over Multi-table Inputs, by Weijia Zhang et al.
QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs
by Weijia Zhang, Vaishali Pal, Jia-Hong Huang, Evangelos Kanoulas, Maarten de Rijke
First submitted to arxiv on: 8 May 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 This paper proposes a novel approach to table summarization, which condenses information from tabular data into concise and comprehensible textual summaries. The existing methods often fail to meet users’ requirements and overlook real-world complexities. To address these limitations, the authors introduce query-focused multi-table summarization, comprising a table serialization module, a summarization controller, and a large language model (LLM). This approach utilizes textual queries and multiple tables to generate query-dependent table summaries tailored to users’ information needs. A comprehensive dataset with 4909 query-summary pairs is presented, each associated with multiple tables. Experimental results demonstrate the effectiveness of the proposed method compared to baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Table summarization helps condense information from tabular data into a concise and easy-to-understand text. Right now, existing methods don’t meet people’s needs and ignore real-world complexities. This paper proposes a new way to summarize tables that takes into account what users are looking for. It uses a combination of techniques, including serializing tables, controlling summaries, and using large language models. The method also considers the complexity of real-world queries. To test this approach, the authors created a big dataset with many query-summary pairs. The results show that their method is better than existing methods at summarizing tables. |
Keywords
» Artificial intelligence » Large language model » Summarization
