# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## Appendix B
Sentiment lexicon extraction: Perform sentiment analysis on each paragraph \(p_{i}\) and extract sentiment words \(w_{ij}\) to form the sentiment lexicon set \(W_{i}\). Calculate the sentiment lexicon density of each paragraph \(s_{i}=\frac{|W_{i}|}{length(p_{i})}\). The sentiment lexicon feature vector is represented as \(E_{emo}=\big{[}\frac{\sum_{i}|W_{i}|}{\sum_{i}length(p_{i})},max(s_{i}),avg(s_ {i})\big{]}\). 3. Character perspective analysis: Extract the subject \(sub_{ij}\) of each paragraph \(p_{i}\) and determine whether each subject is a character reference. Calculate the proportion of character subjects \(r_{i}=\frac{|sub_{ij}|sub_{ij}\in e_{ij}|}{|sub_{ij}|}\). Set a character perspective proportion threshold \(\delta\), and select paragraphs \(P_{pov}=\{p_{i}|r_{i}>\delta\}\) with proportion higher than the threshold as character perspective paragraphs. The character perspective feature vector is represented as \(E_{pov}=[|P_{pov}|,\frac{|P_{pov}|}{|P|}]\). 4. Rhetorical device detection: Define a set of rhetorical devices \(R=\{r_{1},r_{2},\ldots,r_{m}\}\) and design corresponding recognition rules \(t_{kl}\) for each rhetorical device \(r_{k}\), then \(T_{k}=\{t_{k1},\,t_{k2},\,\ldots\}\). Use the rule set \(T_{k}\) to detect whether each paragraph \(p_{i}\) contains rhetorical devices \(r_{k}\), and obtain the occurrence count \(c_{ik}\) of rhetorical devices. The rhetorical device feature vector is represented as \(E_{rhe}=[\sum\limits_{i}c_{i1},\sum\limits_{i}c_{i2},\ldots,\sum\limits_{i}c_ {im}]\). Combine the above four types of feature vectors to obtain the semantic feature vector of the human interest frame: \(\Phi_{human}=\big{[}E_{bio},E_{emo},E_{pov},E_{rhe}\big{]}\). 5. Economic Consequence Frame Feature Extraction 1. Economic lexicon extraction: Construct an economic domain vocabulary dictionary \(D_{eco}=\{w_{1},w_{2},\ldots,w_{n}\}\), including economic terms, financial terms, etc. Calculate the TF-IDF weight of each word \(w_{i}\) in the text within the dictionary \(D_{eco}\), and use it as the economic relevance of that word. Select the top \(k\) words with the highest economic relevance as the economic lexicon features \(E_{term}=[w_{1},w_{2},\ldots,w_{k}]\). 2. Economic data statistics: Define a set of economic data types \(T_{data}=\big{\{}t_{1},t_{2},\ldots,t_{p}\big{\}}\), such as GDP, CPI, unemployment rate, etc. Design corresponding regular expression templates \(r_{i}\) for each data type \(t_{i}\), and extract economic data values \(v_{ij}\) from the news text based on the templates \(r_{i}\) to form the economic data features \(E_{data}=\big{\{}\big{(}t_{i},v_{ij}\big{)}\big{\}}\). 3. Economic impact analysis: Extract event entities \(e_{i}\) from the news text, such as \"new policy introduced\", \"company bankruptcy\", etc. Determine whether there is a causal relationship between each event entity \(e_{i}\) and economic entities \(eco_{j}\), and obtain the causal relationship set \(C=\big{\{}\big{(}e_{i},eco_{j},rel_{ij}\big{)}\big{\}}\), where \(rel_{ij}\)represents the causal relationship type, such as \"promote\", \"inhib\", etc. For each event entity \(e_{i}\), calculate the number of causal relationships between it and economic entities \(n_{i}=\left|\left\{\left(e_{i},eco_{j},rel_{ij}\right)\right.\left|\left(e_{i},eco _{j},rel_{ij}\right)\in C\right.\right\}\right|.\) The economic impact feature vector is represented as \(E_{impact}=\left[n_{1},n_{2},\ldots,n_{q}\right].\) (4) Industry association assessment: Define a set of industry domains \(I=\left\{i_{1},i_{2},\ldots,i_{r}\right\}\) related to the economy, and construct a corresponding set of keywords \(K_{k}=\left\{kw_{k1},kw_{k2},\ldots\right\}\) for each industry domain \(i_{k}\). Calculate the co-occurrence frequency of each event entity \(e_{i}\) with the keywords of each industry in the text, \(co_{ik}=\sum\limits_{j}count(e_{i},kw_{kj})\).