Summary of Event-keyed Summarization, by William Gantt and Alexander Martin and Pavlo Kuchmiichuk and Aaron Steven White
Event-Keyed Summarization
by William Gantt, Alexander Martin, Pavlo Kuchmiichuk, Aaron Steven White
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 introduces event-keyed summarization (EKS), a new task that combines traditional summarization with document-level event extraction to generate contextualized summaries for specific events. The authors create a dataset called MUCSUM, which consists of summaries of all events in the classic MUC-4 dataset, along with various baselines that include both standard pre-trained language models and larger frontier models. The results show that reducing EKS to traditional summarization or structure-to-text generates inferior summaries, making MUCSUM a robust benchmark for this task. Finally, a human evaluation is conducted on reference and model summaries, providing detailed analysis of the results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to summarize text called event-keyed summarization (EKS). It’s like taking a photo of what happened in a story, not just a summary of the whole thing. The authors made a special dataset for this task, using old data from the MUC-4 project. They also tested different ways to do EKS and found that some methods didn’t work as well as others. This means that their new method is reliable and can be used to test how good AI systems are at summarizing events. |
Keywords
* Artificial intelligence * Summarization
