# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## Appendix B
Calculate the occurrence frequency \(t_{i,k}\) of each keyword in each event \(e_{i}\) (denoted as \(k\), where \(k\) represents the \(k\)-th keyword in the dictionary \(D\)). For each causal relation pair \(\left(e_{i},e_{j}\right)\), calculate the sum of keyword frequencies \(t_{i,k}=t_{i,k}+t_{j,k}\). The keyword feature vector is represented as \(E_{keywords}=\left[t_{1,1},t_{1,2},\ldots,t_{ij,k},\ldots\right]\). 4. Context information encoding: Extract the contextual information \(u_{i}\) such as time and location of each event \(e_{i}\) occurrence, then \(U=\left\{u_{1},u_{2},\ldots,u_{l}\right\}\). Convert each piece of contextual information into a feature vector \(u_{i}\), then the contextual information feature matrix is represented as \(E_{context}=\left[u_{1},u_{2},\ldots,u_{l}\right]^{T}\). Concatenate the above four types of feature vectors to obtain the semantic feature vector of the responsibility frame: \(\Phi_{responsibility}=\left[E_{causality},E_{sentiment},E_{keywords},E_{context}\right]\). 3. Conflict Frame Feature Extraction 1. Sentiment lexicon extraction: We use the sentiment analysis module from spaCy to identify words expressing negative emotions such as anger, frustration, and disagreement in the text. While \"conflict\" and \"confrontation\" are not emotions per se, they often indicate underlying emotional states commonly associated with conflict situations. We calculate the sentiment intensity si of each identified word based on the context. Calculate the total number \(N_{s}\) of negative sentiment words and the sum \(S_{s}=\sum_{i=1}^{N_{s}}s_{i}\) of their sentiment intensities \(E_{sentiment}=\left[N_{s},S_{s}\right]\). 2. Opposing entities identification: Through named entity recognition, obtain the opposing entities \(e_{1}\) and \(e_{2}\) For each entity \(e_{i}\), extract its occurrence frequency \(f_{i}\) in the text and its key descriptive words \(d_{ij}\). The opposing entities feature vector is represented as \(E_{entities}=\left[e_{1}(f_{1},d_{11},d_{12},\ldots),e_{2}(f_{2},d_{21},d_{22}, \ldots)\right]\). 3. Argumentation pattern matching: Predefine argumentation pattern templates \(J=\left\{j_{1},j_{2},\ldots,j_{m}\right\}\), such as \"A argued that..\", \"A and B debated..\", etc. Calculate the number \(c_{i}\) of matches for each pattern template \(j_{i}\) in the text. The argumentation pattern feature vector is represented as \(E_{patterns}=\left[c_{1},c_{2},\ldots,c_{m}\right]\). 4. Rhetorical device analysis: Define a set of rhetorical devices \(R=\left\{r_{1},r_{2},\ldots,r_{n}\right\}\) that express conflict, such as \(r_{1}=``metapnor\"\), \(r_{2}=``hyperbole\"\), etc. Design corresponding recognition rules \(r_{i}\) for each rhetorical device. For example,metaphor can be recognized through \"is like\", \"is a\", while hyperbole can be recognized through \"most\", \"always\", etc. Calculate the occurrence frequency \(t_{i}\) of each rhetorical device in the news text. The rhetorical device feature vector is represented as \(E_{rhetoric}=[t_{1},t_{2},\ldots,t_{n}]\). Combine the above four types of feature vectors into the semantic feature vector of the conflict frame: \(\Phi_{conflict}=\big{[}E_{sentiment},E_{entities},E_{patterns},E_{rhetoric} \big{]}\). 4. Human Interest Frame Feature Extraction 1. Character sketch identification: For each paragraph \(p_{i}\) in the text, extract person names, pronouns, and other character references \(e_{ij}\), and calculate the density \(d_{i}=\frac{\sum\limits_{j}e_{ij}}{length(p_{i})}\) of character references in each paragraph, where \(length(p_{i})\) is the number of character references in \(p_{i}\). Set a character sketch density threshold \(\theta\), and select paragraphs \(P_{bio}=\{p_{i}\mid d_{i}>\theta\}\) with density higher than the threshold as character sketch paragraphs. The character sketch feature vector is represented as \(E_{bio}=[|P_{bio}|,\frac{|P_{bio}|}{|P|}]\). 2.