PyTextRank
An implementation of TextRank in Python for use in spaCy pipelines which provides fast, effective phrase extraction from texts, along with extractive summarization. The graph algorithm works independent of a specific natural language and does not require domain knowledge. See (Mihalcea 2004) https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
Example
import spacy import pytextrank # example text text = """Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating sets of solutions for all types of systems are given. These criteria and the corresponding algorithms for constructing a minimal supporting set of solutions can be used in solving all the considered types systems and systems of mixed types.""" # load a spaCy model, depending on language, scale, etc. nlp = spacy.load("en_core_web_sm") # add PyTextRank to the spaCy pipeline nlp.add_pipe("textrank") doc = nlp(text) # examine the top-ranked phrases in the document for phrase in doc._.phrases: print(phrase.text) print(phrase.rank, phrase.count) print(phrase.chunks)
GitHubDerwenAI/pytextrank
Categories pipeline
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