# 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
; simultaneously, based on sentence pattern matching methods, we extract sentences expressing speech acts such as arguments and debates in the news report to further highlight the conflict features; finally, we analyze the rhetorical devices used in the news, such as metaphors and exaggerations, to observe whether they enhance the sense of conflict. Combining the above features, we obtain the semantic feature vector for the conflict frame. D. Human Interest Frame Feature Extraction: Using character profile recognition techniques, we extract paragraphs focusing on personal stories and emotional experiences from the news report to reflect the human interest features; then, through sentiment analysis models, we identify vocabulary expressing character emotions and psychological states in the news, such as \"sad\", \"delighted\", etc., to enhance the shaping of character images; subsequently, we analyze whether the news report attempts to describe events from the character's perspective, such as using a first-person perspective to highlight personal experiences; finally, we detect vivid and figurative rhetorical devices used in the news, such as metaphors and similes, to further evoke readers' empathy. Combining the above features, we obtain the semantic feature vector for the human interest frame. E. Economic Consequences Frame Feature Extraction: Based on vocabulary matching methods, we extract vocabulary related to economy and finance from the news report, such as \"cost\", \"profit\", \"stock price\", etc., to reflect the characteristics of the economic consequences frame; then, we count the various economic data appearing in the news, such as growth rate, inflation rate, GDP, etc., to quantify the economic impact; simultaneously, through causal relationship extraction models, we analyze the specific impact and consequences of the news event on economic operation; finally, combining industry knowledge, we assess the relevance of the news event to specific economic fields or industries to further enrich the analysis of the economic consequences frame. Combining the above features, we obtain the semantic feature vector for the economic consequences frame. F. Morality Frame Feature Extraction: Based on vocabulary matching methods, we extract vocabulary related to moral values such as morality, ethics, and justice from the news report, such as \"justice\", \"morality\", etc., to reflect the characteristics of the morality frame; then, through sentence pattern matching techniques, we extract sentences containing moral judgments and connotations of praise or criticism in the news to further highlight the moral orientation; subsequently, we analyze the rhetorical devices used in the news, such as metaphors and similes, to observe whether they are intended to reinforce moral appeals; finally, combining semantic reasoning techniques, we mine the implicit moral implications in the news report to more comprehensively capture the features of the morality frame. The above four types of features are combined into a semantic feature vector for the morality frame. G. Leadership Frame Feature Extraction: Using character recognition techniques, we extract the leaders involved in the news report, such as politicians, entrepreneurs, etc. ; then, based on a verb dictionary, we extract verbs describing the behavior of these leaders in the news, such as \"decide\", \"call for\", \"promote\", etc., to reflect leadership characteristics; subsequently, through opinion mining models, we identify evaluative descriptions of leaders' abilities, achievements, etc., in the news, such as \"outstanding leadership\", \"excellent achievements\", etc. ; finally, we analyze the rhetorical devices used in the news, such as exaggeration and praise, to observe whether they are intended to highlight the leadership image. Combining the above features, we can obtain the semantic feature vector for the leadership frame. For a more detailed explanation of the feature extraction process for each frame type, please refer to Appendix B. 1.