Compute LexRanks from sentence pair similarities using the page rank algorithm or degree centrality the methods used to compute lexRank are discussed in "LexRank: Graph-based Lexical Centrality as Salience in Text Summarization."
lexRankFromSimil(s1, s2, simil, threshold = 0.2, n = 3,
returnTies = TRUE, usePageRank = TRUE, damping = 0.85,
continuous = FALSE)A character vector of sentence IDs corresponding to the s2 and simil arguments
A character vector of sentence IDs corresponding to the s1 and simil arguments
A numeric vector of similarity values that represents the similarity between the sentences represented by the IDs in s1 and s2.
The minimum simil value a sentence pair must have to be represented in the graph where lexRank is calculated.
The number of sentences to return as the extractive summary. The function will return the top n lexRanked sentences. See returnTies for handling ties in lexRank.
TRUE or FALSE indicating whether or not to return greater than n sentence IDs if there is a tie in lexRank. If TRUE, the returned number of sentences will not be limited to n, but rather will return every sentence with a top 3 score. If FALSE, the returned number of sentences will be <=n. Defaults to TRUE.
TRUE or FALSE indicating whether or not to use the page rank algorithm for ranking sentences. If FALSE, a sentences unweighted centrality will be used as the rank. Defaults to TRUE.
The damping factor to be passed to page rank algorithm. Ignored if usePageRank is FALSE.
TRUE or FALSE indicating whether or not to use continuous LexRank. Only applies if usePageRank==TRUE. If TRUE, threshold will be ignored and lexRank will be computed using a weighted graph representation of the sentences. Defaults to FALSE.
A 2 column dataframe with columns sentenceId and value. sentenceId contains the ids of the top n sentences in descending order by value. value contains page rank score (if usePageRank==TRUE) or degree centrality (if usePageRank==FALSE).
http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html
# NOT RUN {
lexRankFromSimil(s1=c("d1_1","d1_1","d1_2"), s2=c("d1_2","d2_1","d2_1"), simil=c(.01,.03,.5))
# }
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