Summary of Rule-driven News Captioning, by Ning Xu et al.
Rule-driven News Captioning
by Ning Xu, Tingting Zhang, Hongshuo Tian, An-An Liu
First submitted to arxiv on: 8 Mar 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 rule-driven news captioning method generates image descriptions by accurately describing individuals and actions associated with an event, adhering to fundamental rules of news reporting. The approach first designs a news-aware semantic rule incorporating primary actions and roles played by named entities. This rule is then injected into the BART model using the prefix-tuning strategy, guiding it to generate sentences that comply with designated rules. The method achieves effective results on two widely used datasets, GoodNews and NYTimes800k. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re watching a news video or reading an article about a big event. You want to summarize what’s happening in just a few sentences. This paper helps machines do the same thing by creating a system that can generate captions for images based on rules of news reporting. The system first figures out what’s happening in the image and who’s involved, then uses this information to create a summary sentence. The result is a caption that accurately describes the event and its participants. The researchers tested their approach on two big datasets and found it worked really well. |
