def event_based_msc(sentences, output_sent_num = 50):
"""
事件驱动的多语句压缩
:param sentences: 待压缩的输入语句集合
:param output_sent_num: 输出语句的个数，默认50句
:return: 分数#句子
"""
# 构建词图，并执行压缩
# 忽略词数小于8的句子
compresser = panda.word_graph(sentences, nb_words=8, lang='en', punct_tag="PUNCT")
# 获取压缩结果
candidates = compresser.get_compression(output_sent_num)
# 将图保存成文本形式
# compresser.write_dot('graph.dot')
# 对压缩结果进行归一化，并按得分由小到大排序
tmp = []
for score, path in candidates:
tmp.append((score / len(path), path))
# 依据得分进行排序
tmp = sorted(tmp, key = lambda tmp : tmp[0])
# 封装结果返回
results = []
for score, path in tmp:
results.append(str(round(score, 6)) + "#" + ' '.join([u[0] for u in path]) + '\n')
return results
def event_keyphrase_based_msc(sentences, output_sent_num=50):
"""
基于事件来构建词图，基于keyphrase来对输出语句进行reranking
:param sentences: 待压缩的语句
:param output_sent_num: 输出语句的个数
:return:得分#句子
"""
# 构建词图，并执行压缩
# 忽略词数小于8的句子
compresser = panda.word_graph(sentences, nb_words=8, lang='en', punct_tag="PUNCT")
# 获取压缩结果
candidates = compresser.get_compression(output_sent_num)
# 利用keyphrases对压缩结果重新打分
reranker = takahe.keyphrase_reranker(sentences, candidates, lang='en')
reranked_candidates = reranker.rerank_nbest_compressions()
results = []
for score, path in reranked_candidates:
results.append(str(round(score, 6)) + "#" + ' '.join([u[0] for u in path]) + '\n')
return results
