lexRankr: Extractive Text Summariztion in R

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##install from CRAN

#install from this github repo


lexRankr is an R implementation of the LexRank algorithm discussed by Güneş Erkan & Dragomir R. Radev in LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. LexRank is designed to summarize a cluster of documents by proposing which sentences subsume the most information in that particular set of documents. The algorithm may not perform well on a set of unclustered/unrelated set of documents. As the white paper’s title suggests, the sentences are ranked based on their centrality in a graph. The graph is built upon the pairwise similarities of the sentences (where similarity is measured with a modified idf cosine similarity function). The paper describes multiple ways to calculate centrality and these options are available in the R package. The sentences can be ranked according to their degree of centrality or by using the Page Rank algorithm (both of these methods require setting a minimum similarity threshold for a sentence pair to be included in the graph). A third variation is Continuous LexRank which does not require a minimum similarity threshold, but rather uses a weighted graph of sentences as the input to Page Rank.

note: the lexrank algorithm is designed to work on a cluster of documents. LexRank is built on the idea that a cluster of docs will focus on similar topics

note: pairwise sentence similarity is calculated for the entire set of documents passed to the function. This can be a computationally instensive process (esp with a large set of documents)

Basic Usage


df <- tibble(doc_id = 1:3, 
             text = c("Testing the system. Second sentence for you.", 
                      "System testing the tidy documents df.", 
                      "Documents will be parsed and lexranked."))
df %>% 
    unnest_sentences(sents, text) %>% 
    bind_lexrank(sents, doc_id, level = 'sentences') %>% 

More Examples