Summary of Does This Summary Answer My Question? Modeling Query-focused Summary Readers with Rational Speech Acts, by Cesare Spinoso-di Piano and Jackie Chi Kit Cheung
Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts
by Cesare Spinoso-Di Piano, Jackie Chi Kit Cheung
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles query-focused summarization (QFS), where AI models generate summaries in response to user-written queries. The authors note that current QFS systems don’t adequately consider the user’s understanding of the generated summary, leading to underperformance at inference time. To address this, they adapt the Rational Speech Act (RSA) framework to model a reader’s comprehension of a query-focused summary and integrate it with existing QFS generation methods. Specifically, they introduce an answer reconstruction objective that approximates a reader’s understanding by their ability to use the summary to reconstruct the original query’s answer. This allows them to re-rank candidate summaries generated by existing QFS systems and select ones that better align with the corresponding query and reference summary. The study suggests that incorporating user requirements into language generation can improve system performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI-generated summaries more helpful for users. Right now, when you ask a question, AI might give you a summary of the answer, but it’s not always useful because it doesn’t really understand what you’re looking for. The authors developed a new way to make AI generate summaries that are actually helpful by thinking like a person trying to understand the summary. They tested this approach and found that it works better than current methods. This could help us create more useful AI-generated summaries in the future. |
Keywords
» Artificial intelligence » Inference » Summarization
