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from lexrank import STOPWORDS
from lexrank import LexRank as LR
import nltk
from .base_single_doc_model import SingleDocSummModel
class LexRankModel(SingleDocSummModel):
# static variables
model_name = "LexRank"
is_extractive = True
is_neural = False
def __init__(self, data, summary_length=2, threshold=0.1):
super(LexRankModel, self).__init__()
nltk.download("punkt", quiet=True)
corpus = [nltk.sent_tokenize(example) for example in data]
self.lxr = LR(corpus, stopwords=STOPWORDS["en"])
self.summary_length = summary_length
self.threshold = threshold
def summarize(self, corpus, queries=None):
self.assert_summ_input_type(corpus, queries)
documents = [nltk.sent_tokenize(document) for document in corpus]
summaries = [
" ".join(
self.lxr.get_summary(
document, summary_size=self.summary_length, threshold=self.threshold
)
)
for document in documents
]
return summaries
@classmethod
def show_capability(cls):
basic_description = cls.generate_basic_description()
more_details = (
"Works by using a graph-based method to identify the most salient sentences in the document. \n"
"Strengths: \n - Fast with low memory usage \n - Allows for control of summary length \n "
"Weaknesses: \n - Not as accurate as neural methods. \n "
"Initialization arguments: \n "
"- `corpus`: Unlabelled corpus of documents. ` \n "
"- `summary_length`: sentence length of summaries \n "
"- `threshold`: Level of salience required for sentence to be included in summary."
)
print(f"{basic_description} \n {'#'*20} \n {more_details}")
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