Summary of Leveraging Discourse Structure For Extractive Meeting Summarization, by Virgile Rennard et al.
Leveraging Discourse Structure for Extractive Meeting Summarization
by Virgile Rennard, Guokan Shang, Michalis Vazirgiannis, Julie Hunter
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 AI research paper introduces an innovative system for extracting summaries from complex meeting discussions, utilizing discourse structure to pinpoint key information. The proposed GNN-based node classification model leverages semantic relations between utterances in a meeting, represented as discourse graphs, to select the most important statements and combine them into a concise summary. Compared to existing text-based and graph-based summarization systems, the approach demonstrated superior performance on AMI and ICSI datasets, measured by both classification and summarization metrics. Ablation studies provide valuable insights for future NLP applications incorporating discourse analysis theory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI research paper is about a new way to summarize meetings. It uses special diagrams called discourse graphs to figure out what’s important in a meeting conversation. The system then picks out the most important parts and puts them together into a short summary. The results show that this method works better than other methods for summarizing text and graph data. This is useful for future AI applications that want to understand complex conversations. |
Keywords
» Artificial intelligence » Classification » Discourse » Gnn » Nlp » Summarization
