# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## Appendix B
The industry association feature vector is represented as \(E_{inlustry}=\left[co_{11},co_{12},\ldots,co_{1r},co_{21},\ldots,co_{qr}\right]\), that is the association degree between each event entity and each industry. Combine the above four types of features to obtain the complete semantic feature vector of the economic consequence frame: \(\Phi_{economic}=\left[E_{tern},E_{data},E_{impact},E_{industry}\right]\). 2. Morality Frame Feature Extraction 1. [label=(0)]
3. Moral lexicon extraction: Construct a moral domain vocabulary dictionary \(D_{moral}=\left\{w_{1},w_{2},\ldots,w_{n}\right\}\), including words related to morality, ethics, justice, etc. Calculate the occurrence frequency \(f_{i}\) of each word \(w_{i}\) in the text within the dictionary \(D_{moral}\), and use it as the moral relevance of that word. Select the top \(k\) words with the highest moral relevance as the moral lexicon features \(E_{term}=\left[w_{1},w_{2},\ldots,w_{k}\right]\). 4. Moral pattern matching: Define a set of moral judgment sentence patterns \(P_{moral}=\left\{p_{1},p_{2},\ldots,p_{s}\right\}\), such as \"is a virtue\". For each sentence \(s_{i}\) in the text, match the sentence patterns \(P_{moral}\) and obtain \(m_{ij}=match(s_{i},p_{j})\), where \(m_{ij}=1\) indicates a successful match and \(m_{ij}=0\) indicates a failure. The moral pattern feature vector is represented as \(E_{pattern}=\left[m_{11},m_{12},\ldots,m_{1s},m_{21},\ldots,m_{ts}\right]\), where \(t\) is the total number of sentences in the text. 2. Rhetorical device analysis: Define a set of rhetorical devices \(R_{moral}=\left\{r_{1},r_{2},\ldots,r_{u}\right\}\) related to moral appeals, and design corresponding recognition rules \(alg_{k}\) for each rhetorical device \(r_{k}\). For each sentence \(s_{i}\) in the text, use the recognition rules \(alg_{k}\) to determine whether it contains rhetorical devices \(r_{k}\), then \(rh_{ik}=alg_{k}(s_{i})\), where \(rh_{ik}=1\) indicates occurrence and \(rh_{ik}=0\) indicates non-occurrence. The rhetorical device feature vector is represented as \(E_{rhetoric}=\left[rh_{11},rh_{12},\ldots,rh_{1u},rh_{21},\ldots,rh_{ltu}\right]\). 5. Semantic reasoning: Construct a knowledge base \(KB_{moral}\) containing moral common sense, values, etc., represented as a set of triples \((e_{1},r,e_{2})\), where \(e_{1}\) and \(e_{2}\) are entities or concepts, and \(r\) is the relationship between them. Extract entities, concepts, and their relationships from the text to obtain the semantic representation \(G_{news}=\left\{\left(e_{1}^{\prime},r^{\prime},e_{2}^{\prime}\right)\right\}\). Use a knowledge reasoning algorithm to determine whether each triple in \(G_{news}\) can be inferred from the moral knowledge base \(KB_{moral}\), and obtain the reasoning results \(inf_{i}=infer\left(\left(e_{1}^{\prime},\ r^{\prime},\ e_{2}^{\prime}\right),\) where \(inf_{i}=1\) indicates that it can be inferred and \(inf_{i}=0\) indicates that it cannot be inferred. The semantic reasoning feature vector is represented as \(E_{inference}=\left[inf_{1},inf_{2},\ldots,inf_{q}\right]\). Combine the above four types of features into the complete semantic feature vector of the morality frame: \(\Phi_{moral}=\left[E_{term},E_{pattern},E_{rhetoric},E_{inference}\right]\). 6. Leadership Frame Feature Extraction 1. [label=(0)]
7. Leader identification: Obtain the set of person names \(E=\left\{e_{1},e_{2},\ldots,e_{n}\right\}\) in the text through named entity recognition. Construct a leadership position dictionary \(D_{leader}=\left\{j_{1},j_{2},\ldots,j_{m}\right\}\), including leadership position names such as president, prime minister, CEO, etc. Match the leadership position dictionary \(D_{leader}\) in the context of each person name \(e_{i}\). If a match exists, mark it as a leader. The leader feature vector is represented as \(E_{leader}=[l_{1},l_{2},\ldots,l_{n}]\), where \(l_{i}=1\) indicates that the person \(e_{i}\) is a leader, and \(l_{i}=0\) indicates that the person is not a leader.