Summary of Tacoere: Cluster-aware Compression For Event Relation Extraction, by Yong Guan et al.
TacoERE: Cluster-aware Compression for Event Relation Extraction
by Yong Guan, Xiaozhi Wang, Lei Hou, Juanzi Li, Jeff Pan, Jiaoyan Chen, Freddy Lecue
First submitted to arxiv on: 11 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 The proposed TacoERE method addresses the challenge of event relation extraction (ERE) by introducing a cluster-aware compression approach. The model first clusters documents to capture intra-cluster dependencies and then uses cluster summarization to simplify and highlight important text content, mitigating information redundancy and distance between events. Experimental results demonstrate the effectiveness of TacoERE on three ERE datasets using pre-trained language models like RoBERTa and large language models like ChatGPT and GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TacoERE is a new way to help computers understand relationships between events in text. It’s like organizing information into groups to make it easier to find important details. This helps reduce extra information and makes it simpler to see how events are connected, even when they’re far apart. The method was tested on three different datasets and worked well using popular language models. |
Keywords
» Artificial intelligence » Gpt » Summarization
