Summary of Analysis Of Multidomain Abstractive Summarization Using Salience Allocation, by Tohida Rehman et al.
Analysis of Multidomain Abstractive Summarization Using Salience Allocation
by Tohida Rehman, Raghubir Bose, Soumik Dey, Samiran Chattopadhyay
First submitted to arxiv on: 19 Feb 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 This study examines abstractive text summarization through the SEASON technique, which uses salience allocation to enhance summarization. The authors compare SEASON with prominent models like BART, PEGASUS, and ProphetNet on various text summarization tasks using diverse datasets including CNN/Dailymail, SAMSum, Financial-news based Event-Driven Trading (EDT), and a financial dataset from 2020/03/01 to 2021/05/06. The evaluation metrics used include ROUGE, METEOR, BERTScore, and MoverScore. This analysis provides insights into the strengths and weaknesses of each model in summarizing different types of datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how a special technique called SEASON can help summarize text. It compares this technique with other popular methods to see which one does the best job. The researchers use lots of different types of texts, like news articles and financial reports, to test these techniques. They also use special metrics to measure how well each technique works. |
Keywords
» Artificial intelligence » Cnn » Rouge » Summarization
