Summary of Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space, by Seongmin Park et al.
Unsupervised Extractive Dialogue Summarization in Hyperdimensional Space
by Seongmin Park, Kyungho Kim, Jaejin Seo, Jihwa Lee
First submitted to arxiv on: 16 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 A machine learning framework called HyperSum is introduced, which combines the efficiency of traditional lexical summarization methods with the accuracy of neural approaches. This framework exploits the “blessing of dimensionality” to generate representative sentence embeddings, and then uses clustering and medoid extraction to produce summaries that are competitive with state-of-the-art models. HyperSum often outperforms existing summarizers in terms of both summary accuracy and faithfulness, while being significantly faster. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HyperSum is a new way to create summaries from text using machine learning. It takes the best ideas from different approaches and combines them into one efficient framework. This means it can summarize text quickly and accurately, often better than other methods. HyperSum is open-source, so others can use it as a starting point for their own summarization projects. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Summarization
