Summary of A Guide to Effectively Leveraging Llms For Low-resource Text Summarization: Data Augmentation and Semi-supervised Approaches, by Gaurav Sahu et al.
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
by Gaurav Sahu, Olga Vechtomova, Issam H. Laradji
First submitted to arxiv on: 10 Jul 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 research paper proposes two novel methods, MixSumm and PPSL, to leverage large language models (LLMs) like LLaMA-3-70b-Instruct for low-resource text summarization. The first method, MixSumm, employs an LLM-based data augmentation regime that synthesizes high-quality documents by mixing topical information from a small seed set. The second approach, PPSL, uses the same LLM to generate high-quality pseudo-labels in a semi-supervised learning setup. To evaluate these methods, the authors use various datasets, including TweetSumm, WikiHow, and ArXiv/PubMed, and employ L-Eval and ROUGE scores as evaluation metrics. The results demonstrate that MixSumm and PPSL achieve competitive ROUGE scores comparable to fully supervised methods using only 5% of labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper finds new ways to use powerful language models to summarize text even when we don’t have much training data. The authors propose two methods, MixSumm and PPSL, that can generate high-quality summaries by combining information from a small amount of labeled data with the language model’s ability to understand text. They test these methods on different types of texts and show that they work well even when using only a small amount of training data. |
Keywords
» Artificial intelligence » Data augmentation » Language model » Llama » Rouge » Semi supervised » Summarization » Supervised
