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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20021540 |
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Table of Contents:
- <p>The massive increase in digital text has created a huge need for effective, automated summarization systems. Manually summarizing large documents takes significant time and is often not practical. Recent improvements in transformer neural language models have helped with abstractive summarization significantly. In this research, we assess the relative performance of three neural language models for automatic text summarization, specifically BART, T5, and GPT, using the same conditions for each model (compression ratio for conciseness and ROUGE metrics for summary quality). We propose a combined performance metric to compare this trade-off of length versus informativeness. Our experimental results demonstrate encoder-decoder architectures outperform autoregressive architectures in automatic summarization tasks. BART produced the highest quality summaries among all three models evaluated. Our study proposes an evaluation framework and methods for model selection to assist in the efficient development of practical summarization systems.</p>