Description Usage Arguments Value References Examples
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."
1 2 3 | lexRankFromSimil(s1, s2, simil, threshold = 0.2, n = 3,
returnTies = TRUE, usePageRank = TRUE, damping = 0.85,
continuous = FALSE)
|
s1 |
A character vector of sentence IDs corresponding to the |
s2 |
A character vector of sentence IDs corresponding to the |
simil |
A numeric vector of similarity values that represents the similarity between the sentences represented by the IDs in |
threshold |
The minimum simil value a sentence pair must have to be represented in the graph where lexRank is calculated. |
n |
The number of sentences to return as the extractive summary. The function will return the top |
returnTies |
|
usePageRank |
|
damping |
The damping factor to be passed to page rank algorithm. Ignored if |
continuous |
|
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
1 | lexRankFromSimil(s1=c("d1_1","d1_1","d1_2"), s2=c("d1_2","d2_1","d2_1"), simil=c(.01,.03,.5))
|
sentenceId value
1 d1_2 0.5
2 d2_1 0.5
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