# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## Appendix B  
In this appendix, we provide a detailed explanation of the feature extraction process for each frame type. For named entity recognition, sentiment analysis, and dependency parsing tasks across all relevant frame types, we utilize the spaCy library, which provides robust and efficient natural language processing capabilities. 1. Fact Frame Feature Extraction 1. [label=(0)]
2. Keyword extraction: Define part-of-speech patterns \(P=\{p_{1},p_{2},\ldots,p_{m}\}\) for factual keywords, such as \(p_{1}=\text{``who/WP''}\), \(p_{2}=\text{``what/WP''}\), etc. Match the tokenized and part-of-speech tagged results. Extract the words \(w_{j}\) corresponding to the matched pattern \(p_{i}\) positions \(j\) as the set of factual keywords \(K_{i}\). The keyword feature vector is represented as \(E_{keyword}=[K_{1},K_{2},\ldots,K_{m}]\). 3. Named entity recognition: Perform named entity recognition to obtain the set of named entities \(E=\{e_{1},e_{2},\ldots,e_{k}\}\). For each named entity type \(c_{i}\), calculate its occurrence frequency \(f_{i}\) in the set \(E\), then \(F=\{f_{1},f_{2},\ldots,f_{l}\}\), where \(l\) is the number of named entity types. The named entity feature vector can be represented as \(E_{entity}=[E,F]\). 4. Numerical information statistics: Use regular expressions to match numerical information in the news text, obtaining the set of numerical information \(N=\left\{n_{1},n_{2},\ldots,n_{q}\right\}\). For each type \(u_{i}\) of numerical information, calculate its occurrence frequency \(g_{i}\) in the set \(N\), then \(G=[g_{1},g_{2},\ldots,g_{r}]\), where \(r\) is the number of numerical information types. The numerical information feature vector can be represented as \(E_{numeric}=[N,G]\). 5. Factual statement extraction: Extract the subject-verb-object structure of each sentence \(s_{i}\) in the text and determine whether it is a factual statement. Extract the set of factual statements \(D=\left\{d_{1},d_{2},\ldots,d_{v}\right\}\) and calculate the proportion of factual statements \(r=\frac{|D|}{|S|}\), where \(|S|\) is the total number of sentences in the text. The factual statement feature vector can be represented as \(E_{statement}=[D,r]\). Combine the above four types of features into the semantic feature vector of the factual frame: \(\Phi_{fact}=\left[E_{keyword},E_{entity},E_{numeric},E_{statement}\right]\). 2. Responsibility Frame Feature Extraction 1. Causal relation extraction: Perform dependency parsing on the text to obtain dependency structure triples in the form. Based on dependency relation patterns \(M=\left\{m_{1},m_{2},\ldots,m_{p}\right\}\), such as \(m_{1}=\left(``nsubj\"\), \"_dobj\", \"cause_\"\right), \(m_{2}\) = (\"nsubj\", \"prep\", \"_pobj\"_), match the \"cause-effect\" semantic relation pairs \(\left(e_{i},e_{j}\right)\) and determine the causal relation type \(c_{k}\in C\)\(\left(k=1,2,\ldots,n\right)\) they belong to. Calculate the frequency \(f_{k}\) of each causal relation type \(c_{k}\), then \(E_{causality}=\left[f_{1},f_{2},\ldots,f_{n}\right]\). 2. Sentiment analysis: Using the sentiment analysis module provided by spaCy, we perform sentiment analysis on the events \(e_{i}\) and \(e_{j}\), obtaining scores \(s_{i}\) and \(s_{j}\). The spaCy sentiment analyzer classifies text into positive, negative, or neutral categories, providing a comprehensive understanding of the emotional tone associated with each event. The sentiment feature of the causal relation pair \(\left(e_{i},e_{j}\right)\) is \(s_{i,j}=s_{i}+s_{j}\). Calculate the average sentiment score \(s\) of all relation pairs as the overall sentiment orientation feature, then \(E_{sentiment}=\left[ \begin{array}{cc}\overline{s},s_{1,2},s_{1,3},\ldots,s_{ i,j},\ldots\end{array} \right]\). 3. Responsibility keyword statistics: Construct a responsibility keyword dictionary \(D=\left\{w_{1},w_{2},\ldots,w_{m}\right\}\), including words such as \"responsibility\", \"accountability\", \"investigate\", etc.