Summary of Label-free Topic-focused Summarization Using Query Augmentation, by Wenchuan Mu and Kwan Hui Lim
Label-Free Topic-Focused Summarization Using Query Augmentation
by Wenchuan Mu, Kwan Hui Lim
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 A novel approach called Augmented-Query Summarization (AQS) is introduced for topic-focused summarization without requiring extensive labelled datasets or significant computational power. This technique leverages query augmentation and hierarchical clustering to facilitate the transferability of machine learning models to the task of summarization, eliminating the need for topic-specific training. AQS demonstrates its potential as a cost-effective solution in data-rich environments through real-world tests, generating relevant and accurate summaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers develop a new way to summarize text without needing lots of labeled data or powerful computers. They call it Augmented-Query Summarization (AQS). AQS uses a combination of techniques to make machine learning models work well for summarization tasks, even if they’re not specifically trained for that task. This makes it possible to summarize large amounts of text quickly and accurately. |
Keywords
» Artificial intelligence » Hierarchical clustering » Machine learning » Summarization » Transferability
