Summary of Ranksum An Unsupervised Extractive Text Summarization Based on Rank Fusion, by A. Joshi et al.
RankSum An unsupervised extractive text summarization based on rank fusion
by A. Joshi, E. Fidalgo, E. Alegre, R. Alaiz-Rodriguez
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes Ranksum, a novel approach to extractive text summarization for single documents. The method fuses four multi-dimensional sentence features – topic information, semantic content, significant keywords, and position – to rank sentences according to their significance. The scores are generated unsupervisedly, with a labeled document set required to learn the fusion weights. Ranksum outperforms existing state-of-the-art methods on publicly available summarization datasets CNN/DailyMail and DUC 2002. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make summaries of single documents. It takes four things about each sentence – what it’s talking about, how important the words are, what keywords appear, and where the sentence is in the document – and combines them to rank sentences by how good they are for the summary. The method doesn’t need labels or training, but does need a labeled set of documents to figure out how to weight each feature. This new approach works better than others on big datasets. |
Keywords
* Artificial intelligence * Cnn * Summarization
