# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 3 News Text Summarization Model Based on Framing Theory
### News Framing Recognition Module (NFRM) Based on Deep Learning
#### 3.2.3 Design of NFRM  
We design a deep learning-based news frame recognition model to automatically extract semantic features from news text and analyze and identify the frame types used in the news, thus guiding the generation of subsequent summaries. The architecture of NFRM is shown in Figure 2.  
1. Text Preprocessing  
We use the existing natural language processing toolkit spaCy [52] to segment, tokenize, and perform part-of-speech tagging on the raw news text and utilize NER tools  
Figure 2: Architecture of NFRM. to identify named entities in the text. Subsequently, we perform language normalization processing on the text, including removing stop words, converting case, handling punctuation marks, etc., laying the foundation for subsequent feature extraction and model construction. Text Semantic Representation  
We use the encoder part of the transformer model [22] as the text semantic feature extractor. The core of the transformer is the multi-head attention mechanism, which is used to capture the relationships between different semantic features. For a detailed explanation of the attention and multi-head attention mechanisms, we refer readers to the original paper by [PERSON] et al. [22]. After \(N\) layers of transformer encoder computation, we finally obtain the contextual semantic representation matrix \(H\in\mathbb{R}^{n\times\text{d}}\) of the text sequence. The key innovation of our NFRM lies in the frame element feature extraction process. This section introduces our approach to extracting semantic features specific to each frame type, which forms the foundation of our frame-aware summarization model. A. Fact Frame Feature Extraction: Utilizing tokenization and part-of-speech tagging techniques, we extract key information such as \"who, what, when, where\" from the news text to reflect the objective facts of the news report; then, through named entity recognition models, we identify named entities involved in the news, such as person names, place names, organizations, etc., to further enrich the factual information; simultaneously, we count the various numerical information appearing in the news report, such as quantity, time, amount, etc., which are also important indicators reflecting objective facts; finally, we extract factual statement sentences from the text and calculate their proportion to further highlight the factual orientation of the news report. The above four types of features are combined into a semantic feature vector for the fact frame. B. Responsibility Frame Feature Extraction: Using causal relationship extraction models, we analyze the causes and consequences of events in the news report to determine responsibility attribution; then, through sentiment analysis techniques, we identify the stances of various subjects (such as government, enterprises, the public, etc.) in the news to further understand responsibility attribution; at the same time, based on keyword matching methods, we extract relevant vocabulary such as \"responsibility\", \"accountability\", \"investigation\", etc., in the news report, which can also reflect the characteristics of the responsibility frame; finally, combining contextual factors such as the time, place, and nature of the event in the report, we analyze the specific context of responsibility attribution to more comprehensively capture the features of the responsibility frame. The above four types of features are combined into a semantic feature vector for the responsibility frame. C. Conflict Frame Feature Extraction: Using sentiment analysis models, we extract vocabulary expressing negative emotions such as conflict and confrontation in the news report, such as \"dispute\", \"confrontation\", etc. ; then, through subject identification techniques, we identify the opposing parties involved in the news, such as political opponents, social groups, etc.