def summarize(self, text, n_sents=3):
""" Summarize a given text and get top sentences """
try:
prediction = dict()
if text:
if self.lang_code in self.valid_langs:
if Utility.get_doc_length(text) > self.n_words:
# generate sentences, normalized sentences from text
sents, norm_sents = self.p.text_preprocessing(text)
# generate doc-term-matrix, term-doc-matrix
dt_matrix = self.generate_doc_term_matrix(norm_sents)
td_matrix = self.generate_term_doc_matrix(dt_matrix)
if self.method == "LSA":
lsa = LSA(self.k, td_matrix)
term_topic_matrix, singular_values, topic_doc_matrix = lsa.u, lsa.s, lsa.vt
# remove singular values below given treshold
singular_values = lsa.filter_singular_values(
singular_values, self.sv_threshold)
# get salience scores from top singular values & topic document matrix
salience_scores = lsa.get_salience_scores(
singular_values, topic_doc_matrix)
# get the top sentence indices for summarization
top_sentence_indices = lsa.get_top_sent_indices(
salience_scores, n_sents)
summary = self.generate_summary(
sents, top_sentence_indices)
elif self.method == "TEXT_RANK":
tr = TextRank(dt_matrix, td_matrix)
# build similarity graph
similarity_matrix = tr.similiarity_matrix
similarity_graph = tr.get_similarity_graph(
similarity_matrix)
# compute pagerank scores for all sentences
ranked_sents = tr.rank_sentences(similarity_graph)
# get the top sentence indices for summarization
top_sentence_indices = tr.get_top_sentence_indices(
ranked_sents, n_sents)
summary = self.generate_summary(
sents, top_sentence_indices)
else:
return "no method found"
# apply cleaning for readability
summary = Utility.remove_multiple_whitespaces(summary)
summary = Utility.remove_trailing_whitespaces(summary)
prediction["summary"] = summary
prediction["message"] = "successful"
else:
return "required at least {} words".format(
self.n_words)
else:
return "language not supported".format()
else:
return "required textual content"
return prediction
except Exception:
logging.error("exception occured", exc_info=True)
