Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users

• URL: http://arxiv.org/abs/2503.10675v1
• Date: Mon, 10 Mar 2025 19:08:36 GMT
• Title: Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users
• Authors: Mehmet Samet Duran, Tevfik Aytekin,
• Abstract summary: Controlling readability of textual data is an important element for creating summaries for different audiences.<n>We create our own custom dataset and train a model with our custom architecture.<n>Our method ensures that readability levels are effectively controlled while maintaining accuracy and coherence.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, automatic text summarization has witnessed significant advancement, particularly with the development of transformer-based models. However, the challenge of controlling the readability level of generated summaries remains an under-explored area, especially for languages with complex linguistic features like Turkish. This gap has the effect of impeding effective communication and also limits the accessibility of information. Controlling readability of textual data is an important element for creating summaries for different audiences with varying literacy and education levels, such as students ranging from primary school to graduate level, as well as individuals with diverse educational backgrounds. Summaries that align with the needs of specific reader groups can improve comprehension and engagement, ensuring that the intended message is effectively communicated. Furthermore, readability adjustment is essential to expand the usability of summarization models in educational and professional domains. Current summarization models often don't have the mechanisms to adjust the complexity of their outputs, resulting in summaries that may be too simplistic or overly complex for certain types of reader groups. Developing adaptive models that can tailor content to specific readability levels is therefore crucial. To address this problem, we create our own custom dataset and train a model with our custom architecture. Our method ensures that readability levels are effectively controlled while maintaining accuracy and coherence. We rigorously compare our model to a supervised fine-tuned baseline, demonstrating its superiority in generating readability-aware summaries.

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