Language Models for News Summarization: A Comparative Study of Performance

The Performance of Language Models in News Summarization

The constantly growing flood of information, especially in the news sector, poses an increasing challenge for efficient information intake. Automated summarization of news articles using Artificial Intelligence (AI) offers a promising solution. A recent study examines the capabilities of 20 different language models, including both large, established models and smaller, more resource-efficient alternatives, in the field of news summarization.

Methodology and Data Basis

The study focuses on zero-shot and few-shot learning scenarios to evaluate the models' ability to generalize and adapt to new tasks. Three different datasets with news articles in various styles serve as the basis for the investigation. The evaluation of the generated summaries is carried out using a combination of automatic metrics, human evaluation, and assessment by another large language model (LLM-as-a-Judge) to obtain the most comprehensive picture of the quality.

Surprising Results in Few-Shot Learning

An interesting result of the study is that the inclusion of examples in the few-shot learning setting did not necessarily lead to an improvement in the quality of the summaries. In some cases, the performance of the models even deteriorated. This is attributed to the quality of the "gold standard" summaries used as references, which can negatively influence the models. This aspect underscores the importance of high-quality training data for the effective training of language models.

Dominance of Large Language Models and Promising Alternatives

As expected, the large language models GPT-3.5-Turbo and GPT-4 dominated the benchmarks due to their advanced capabilities. However, the performance of some smaller, publicly available models like Qwen1.5-7B, SOLAR-10.7B-Instruct-v1.0, Meta-Llama-3-8B, and Zephyr-7B-Beta is noteworthy. These models showed promising results and position themselves as competitive alternatives to the more resource-intensive large models in the field of news summarization.

Outlook and Significance for Practical Application

The results of this study provide valuable insights into the current capabilities of language models in news summarization. The identification of powerful, smaller models opens up new possibilities for the use of AI-based text summarization in applications with limited resources. The challenges in few-shot learning also highlight the need for further research to optimize training data and methods. The development of efficient and reliable procedures for automated news summarization contributes to managing the information overload and facilitating access to relevant information. For companies like Mindverse, which specialize in the development of AI solutions, these findings are of great importance for developing innovative and customized applications for customers, for example in the area of automated content creation or the development of intelligent chatbots and knowledge databases.

Bibliographie: - https://arxiv.org/abs/2501.18128 - https://arxiv.org/html/2501.18128v1 - https://openaccess.tau.edu.tr/xmlui/handle/20.500.12846/1416 - https://www.chatpaper.com/chatpaper/paper/103686 - https://github.com/monologg/nlp-arxiv-daily - https://paperreading.club/page?id=280915 - https://2024.naacl.org/program/accepted_papers/ - https://2024.aclweb.org/program/finding_papers/ - https://www.researchgate.net/publication/376644591_Unraveling_the_landscape_of_large_language_models_a_systematic_review_and_future_perspectives - https://www.bfdi.bund.de/SharedDocs/Downloads/DE/Berlin-Group/20241206-WP-LLMs.pdf?__blob=publicationFile&v=2