# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 1 Introduction  
In the context of globalization, both the speed and scale of information dissemination are increasing at an unprecedented, exponential rate. A forecast study by the International Data Corporation (IDC) predicts that the total global data volume will grow from 33 ZB in 2018 to 175 ZB by 2025 [1]. Particularly during major global events (e.g., the Olympic Games, financial crises, public health emergencies), news reports flood in like tidal waves, making it especially challenging for the public to capture and understand key information regarding event developments, impacts, and countermeasures in a short period. This predicament not only underscores the necessity of improving information filtering efficiency and in-depth analysis capabilities but also drives news text summarization technology to evolve and keep pace with the times. It continuously enhances its ability to accurately extract and efficiently disseminate core information to meet the needs of readers in an era of information explosion. Although existing news text summarization methods have made some progress in combating information overload, most focus on statistics at the lexical and sentence level and the extraction of linguistic features [2; 3; 4; 5; 6], often overlooking the deep-level semantic content and situational frames implied in news texts. News reports are not simply a compilation of facts; they usually embed multiple attributes, such as specific social, cultural,and ideological aspects, which largely shape the topic selection, discourse construction, and meaning conveyance of the news [7,8]. In this context, a news frame can be defined as a conceptual structure used by journalists to present and organize information, shaping the way events and issues are interpreted. Frames help highlight certain aspects of a story while downplaying others, thus influencing how audiences understand and respond to news content. Many researchers have used various computational tools to capture frames in text corpora, from topic models [9] to dictionary methods [10], semantic networks [11], or supervised machine learning models [12]. [PERSON] et al. [13] even compared frame identification methods with different degrees of supervision, but few researchers have conducted relevant research on text summarization based on these foundations. Therefore, exploring how to introduce framing theory into the news text summarization process, generating summaries that contain factual information and reflect frame characteristics, and thus improving the quality and accuracy of news summaries, is a research topic that urgently needs further exploration and innovative applications. The concept of framing theory will be further elaborated in Section 2.2. This paper takes the CNN/Daily Mail dataset as the research object and innovatively proposes an automatic news text summarization model based on framing theory. The model aims to provide strong technical support and theoretical basis for news dynamics mastery and information attention prediction while promoting the practical application of framing theory in the field of news text summarization. Specifically, this paper designs a deep learning-based news frame identification model that can automatically identify frame elements from the text and then construct an encoder-decoder summary generation model that integrates frame information. This model incorporates the frame identification results into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report and maintains high information coverage, readability, and objectivity. Through empirical research on a large-scale news corpus, we qualitatively and quantitatively compared and analyzed the performance of the proposed frame-aware summarization model with traditional models. The results show that our proposed model significantly improves summary quality and better maintains the accuracy and completeness of news facts.