A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| author | Sahu, Gaurav Vechtomova, Olga Laradji, Issam H. |
| author_facet | Sahu, Gaurav Vechtomova, Olga Laradji, Issam H. |
| contents | Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference time to generate summaries directly; however, such approaches often suffer from inconsistent LLM outputs and are difficult to adapt to domain-specific data in low-resource scenarios. In this work, we propose two novel methods to effectively utilize LLMs for low-resource text summarization: 1) MixSumm, an LLM-based data augmentation regime that synthesizes high-quality documents (short and long) for few-shot text summarization, and 2) PPSL, a prompt-based pseudolabeling strategy for sample-efficient semi-supervised text summarization. Specifically, MixSumm leverages the open-source LLaMA-3-70b-Instruct model to generate new documents by mixing topical information derived from a small seed set, and PPSL leverages the LLaMA-3-70b-Instruct model to generate high-quality pseudo-labels in a semi-supervised learning setup. We evaluate our methods on the TweetSumm, WikiHow, and ArXiv/PubMed datasets and use L-Eval, a LLaMA-3-based evaluation metric, and ROUGE scores to measure the quality of generated summaries. Our experiments on extractive and abstractive summarization show that MixSumm and PPSL achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07341 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches Sahu, Gaurav Vechtomova, Olga Laradji, Issam H. Computation and Language Artificial Intelligence Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference time to generate summaries directly; however, such approaches often suffer from inconsistent LLM outputs and are difficult to adapt to domain-specific data in low-resource scenarios. In this work, we propose two novel methods to effectively utilize LLMs for low-resource text summarization: 1) MixSumm, an LLM-based data augmentation regime that synthesizes high-quality documents (short and long) for few-shot text summarization, and 2) PPSL, a prompt-based pseudolabeling strategy for sample-efficient semi-supervised text summarization. Specifically, MixSumm leverages the open-source LLaMA-3-70b-Instruct model to generate new documents by mixing topical information derived from a small seed set, and PPSL leverages the LLaMA-3-70b-Instruct model to generate high-quality pseudo-labels in a semi-supervised learning setup. We evaluate our methods on the TweetSumm, WikiHow, and ArXiv/PubMed datasets and use L-Eval, a LLaMA-3-based evaluation metric, and ROUGE scores to measure the quality of generated summaries. Our experiments on extractive and abstractive summarization show that MixSumm and PPSL achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
| title | A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.07341 |