Summary of Leveraging the Power Of Llms: a Fine-tuning Approach For High-quality Aspect-based Summarization, by Ankan Mullick et al.
Leveraging the Power of LLMs: A Fine-Tuning Approach for High-Quality Aspect-Based Summarization
by Ankan Mullick, Sombit Bose, Rounak Saha, Ayan Kumar Bhowmick, Aditya Vempaty, Pawan Goyal, Niloy Ganguly, Prasenjit Dey, Ravi Kokku
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The paper explores the potential of fine-tuning large language models (LLMs) for aspect-based summarization, a task that involves generating summaries focused on specific aspects within a document. The authors hypothesize that this approach will enable LLMs to effectively identify and extract aspect-related information, leading to superior quality aspect-based summaries compared to the state-of-the-art. To evaluate the impact of fine-tuning, the authors use a publicly available domain-specific aspect based summary dataset and compare the performance of fine-tuned LLMs against competing aspect-based summarization methods and vanilla counterparts of the fine-tuned LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big language models to help people quickly get the important points from long documents. The authors want to see if they can make these models better at finding specific information within a document by training them on certain types of data. They think this will lead to better summaries that focus on what’s most important. |
Keywords
» Artificial intelligence » Fine tuning » Summarization
