Unraveling the Capabilities of Language Models in News Summarization
Given the recent introduction of multiple language models and the ongoing
demand for improved Natural Language Processing tasks, particularly
summarization, this work provides a comprehensive benchmarking of 20 recent
language models, focusing on smaller ones for the news summarization task. In
this work, we systematically test the capabilities and effectiveness of these
models in summarizing news article texts which are written in different styles
and presented in three distinct datasets. Specifically, we focus in this study
on zero-shot and few-shot learning settings and we apply a robust evaluation
methodology that combines different evaluation concepts including automatic
metrics, human evaluation, and LLM-as-a-judge. Interestingly, including
demonstration examples in the few-shot learning setting did not enhance models'
performance and, in some cases, even led to worse quality of the generated
summaries. This issue arises mainly due to the poor quality of the gold
summaries that have been used as reference summaries, which negatively impacts
the models' performance. Furthermore, our study's results highlight the
exceptional performance of GPT-3.5-Turbo and GPT-4, which generally dominate
due to their advanced capabilities. However, among the public models evaluated,
certain models such as Qwen1.5-7B, SOLAR-10.7B-Instruct-v1.0, Meta-Llama-3-8B
and Zephyr-7B-Beta demonstrated promising results. These models showed
significant potential, positioning them as competitive alternatives to large
models for the task of news summarization.
679e04b792d873dfa23d0ba6
Abdurrahman Odabaşı
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2025-02-03T08:14:51.873Z
647d79a736e109abce419102
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Abdurrahman Odabaşı
user
odabashi
679e04b792d873dfa23d0ba7
Göksel Biricik
Unraveling the Capabilities of Language Models in News Summarization
Given the recent introduction of multiple language models and the ongoing
demand for improved Natural Language Processing tasks, particularly
summarization, this work provides a comprehensive benchmarking of 20 recent
language models, focusing on smaller ones for the news summarization task. In
this work, we systematically test the capabilities and effectiveness of these
models in summarizing news article texts which are written in different styles
and presented in three distinct datasets. Specifically, we focus in this study
on zero-shot and few-shot learning settings and we apply a robust evaluation
methodology that combines different evaluation concepts including automatic
metrics, human evaluation, and LLM-as-a-judge. Interestingly, including
demonstration examples in the few-shot learning setting did not enhance models'
performance and, in some cases, even led to worse quality of the generated
summaries. This issue arises mainly due to the poor quality of the gold
summaries that have been used as reference summaries, which negatively impacts
the models' performance. Furthermore, our study's results highlight the
exceptional performance of GPT-3.5-Turbo and GPT-4, which generally dominate
due to their advanced capabilities. However, among the public models evaluated,
certain models such as Qwen1.5-7B, SOLAR-10.7B-Instruct-v1.0, Meta-Llama-3-8B
and Zephyr-7B-Beta demonstrated promising results. These models showed
significant potential, positioning them as competitive alternatives to large
models for the task of news summarization.
A benchmark of smaller language models for news summarization in zero-shot and few-shot settings highlights GPT-3.5-Turbo and GPT-4 as top performers, while also identifying Qwen1.5-7B, SOLAR-10.7B-Instruct-v1.0, Meta-Llama-3-8B, and Zephyr-7B-Beta as promising alternatives.
zero-shot few-shot learning LLM-as-a-judge GPT-3.5-Turbo GPT-4 Qwen1.5-7B SOLAR-10.7B-Instruct-v1.0 Meta-Llama-3-8B Zephyr-7B-Beta