# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 5 Conclusions  
This paper innovatively introduces the framing theory from news communication into the field of natural language processing for the task of news text summarization. We propose an automatic news text summarization model based on framing theory. By integrating the two subtasks of news framing identification and summary generation, the model can automatically extract framing elements from news reports and generate high-quality summaries that are factual, comprehensive, and frame relevant. Experiments on the standard CNN/Daily Mail dataset show that our proposed method significantly outperforms existing text summarization baseline models on various evaluation metrics, confirming the applicability and effectiveness of framing theory in explaining and guiding news report summarization. Moreover, manual evaluation also indicates that incorporating framing semantics not only helps improve summary quality but also better aligns with human cognitive habits, showing broad prospects in practical application scenarios such as news aggregation, public opinion analysis, and decision support. While our experimental results demonstrate significant improvements in news text summary quality using our framing theory-based model, we acknowledge certain limitations in our approach. Firstly, the currently available manually annotated framing corpora are relatively limited in scale, and the high dependence on training data may limit the generalization ability of the model. Future work needs to explore more efficient and economical framing annotation methods, such as crowdsourcing annotation, few-shot learning, unsupervised corpus mining, etc., to expand the scale of available framing corpora. Secondly, the existing frame-aware summarization models focus more on improving the automatic evaluation metrics of summaries and still lack in-depth explanations of the internal logic and causal mechanisms of summary generation. In the future, it is necessary to draw on the latest advances in explainable artificial intelligence, characterize feature representations in the framing semantic space, highlight framing reasoning chains, and introduce human-computer interaction feedback mechanisms to empower users with the ability to actively adjust framing preferences, ultimately achieving explainable and controllable intelligent summarization. Furthermore, it could also explore the integration of reinforcement learning techniques, which have shown promise in optimizing complex systems [64; 65; 66], to further enhance the adaptability and efficiency of our frame-aware summarization model. Methodology, X.Z. ; validation, Q.W., B.Z. and J.L. ; formal analysis, Q.W. and B.Z. ; investigation, X.Z. ; data curation, Q.W. and J.L. ; writing--original draft preparation, X.Z. and Q.W. ; writing--review and editing, X.Z. and P.Z. ; visualization, Q.W. ; supervision, P.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (No. 2022 YFC3302100). Not applicable. Data Availability Statement: This study utilized three publicly available datasets: The CNN/Daily Mail dataset (non-anonymous version) is available at [[https://github.com/abisee/cnn-dailymail](https://github.com/abisee/cnn-dailymail)]([https://github.com/abisee/cnn-dailymail](https://github.com/abisee/cnn-dailymail)), accessed on 10 April 2024. The BBC News Summary dataset can be accessed on Kaggle at [[https://www.kaggle.com/datasets/pariza/bbc-news-summary](https://www.kaggle.com/datasets/pariza/bbc-news-summary)]([https://www.kaggle.com/datasets/pariza/bbc-news-summary](https://www.kaggle.com/datasets/pariza/bbc-news-summary)), accessed on 13 June 2024.