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rouge_score.py
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rouge_score.py
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# -*- coding: utf-8 -*-
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ROUGE Metric Implementation
This is a very slightly version of:
https://github.com/pltrdy/seq2seq/blob/master/seq2seq/metrics/rouge.py
---
ROUGe metric implementation.
This is a modified and slightly extended verison of
https://github.com/miso-belica/sumy/blob/dev/sumy/evaluation/rouge.py.
"""
from __future__ import absolute_import
from __future__ import division, print_function, unicode_literals
import itertools
def _get_ngrams(n, text):
"""Calcualtes n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams
"""
ngram_set = set()
text_length = len(text)
max_index_ngram_start = text_length - n
for i in range(max_index_ngram_start + 1):
ngram_set.add(tuple(text[i:i + n]))
return ngram_set
def _split_into_words(sentences):
"""Splits multiple sentences into words and flattens the result"""
return list(itertools.chain(*[_.split(" ") for _ in sentences]))
def _get_word_ngrams(n, sentences):
"""Calculates word n-grams for multiple sentences.
"""
assert len(sentences) > 0
assert n > 0
words = _split_into_words(sentences)
return _get_ngrams(n, words)
def _len_lcs(x, y):
"""
Returns the length of the Longest Common Subsequence between sequences x
and y.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y
"""
table = _lcs(x, y)
n, m = len(x), len(y)
return table[n, m]
def _lcs(x, y):
"""
Computes the length of the longest common subsequence (lcs) between two
strings. The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of words
Returns:
Table of dictionary of coord and len lcs
"""
n, m = len(x), len(y)
table = dict()
for i in range(n + 1):
for j in range(m + 1):
if i == 0 or j == 0:
table[i, j] = 0
elif x[i - 1] == y[j - 1]:
table[i, j] = table[i - 1, j - 1] + 1
else:
table[i, j] = max(table[i - 1, j], table[i, j - 1])
return table
def _recon_lcs(x, y):
"""
Returns the Longest Subsequence between x and y.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns:
sequence: LCS of x and y
"""
i, j = len(x), len(y)
table = _lcs(x, y)
def _recon(i, j):
"""private recon calculation"""
if i == 0 or j == 0:
return []
elif x[i - 1] == y[j - 1]:
return _recon(i - 1, j - 1) + [(x[i - 1], i)]
elif table[i - 1, j] > table[i, j - 1]:
return _recon(i - 1, j)
else:
return _recon(i, j - 1)
recon_tuple = tuple(map(lambda x: x[0], _recon(i, j)))
return recon_tuple
def rouge_n(evaluated_sentences, reference_sentences, n=2):
"""
Computes ROUGE-N of two text collections of sentences.
Sourece: http://research.microsoft.com/en-us/um/people/cyl/download/
papers/rouge-working-note-v1.3.1.pdf
Args:
evaluated_sentences: The sentences that have been picked by the
summarizer
reference_sentences: The sentences from the referene set
n: Size of ngram. Defaults to 2.
Returns:
A tuple (f1, precision, recall) for ROUGE-N
Raises:
ValueError: raises exception if a param has len <= 0
"""
if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
raise ValueError("Collections must contain at least 1 sentence.")
evaluated_ngrams = _get_word_ngrams(n, evaluated_sentences)
reference_ngrams = _get_word_ngrams(n, reference_sentences)
reference_count = len(reference_ngrams)
evaluated_count = len(evaluated_ngrams)
# Gets the overlapping ngrams between evaluated and reference
overlapping_ngrams = evaluated_ngrams.intersection(reference_ngrams)
overlapping_count = len(overlapping_ngrams)
# Handle edge case. This isn't mathematically correct, but it's good enough
if evaluated_count == 0:
precision = 0.0
else:
precision = overlapping_count / evaluated_count
if reference_count == 0:
recall = 0.0
else:
recall = overlapping_count / reference_count
f1_score = 2.0 * ((precision * recall) / (precision + recall + 1e-8))
return {"f": f1_score, "p": precision, "r": recall}
def _union_lcs(evaluated_sentences, reference_sentence, prev_union=None):
"""
Returns LCS_u(r_i, C) which is the LCS score of the union longest common
subsequence between reference sentence ri and candidate summary C.
For example:
if r_i= w1 w2 w3 w4 w5, and C contains two sentences: c1 = w1 w2 w6 w7 w8
and c2 = w1 w3 w8 w9 w5, then the longest common subsequence of r_i and c1
is "w1 w2" and the longest common subsequence of r_i and c2 is "w1 w3 w5".
The union longest common subsequence of r_i, c1, and c2 is "w1 w2 w3 w5"
and LCS_u(r_i, C) = 4/5.
Args:
evaluated_sentences: The sentences that have been picked by the
summarizer
reference_sentence: One of the sentences in the reference summaries
Returns:
float: LCS_u(r_i, C)
ValueError:
Raises exception if a param has len <= 0
"""
if prev_union is None:
prev_union = set()
if len(evaluated_sentences) <= 0:
raise ValueError("Collections must contain at least 1 sentence.")
lcs_union = prev_union
prev_count = len(prev_union)
reference_words = _split_into_words([reference_sentence])
combined_lcs_length = 0
for eval_s in evaluated_sentences:
evaluated_words = _split_into_words([eval_s])
lcs = set(_recon_lcs(reference_words, evaluated_words))
combined_lcs_length += len(lcs)
lcs_union = lcs_union.union(lcs)
new_lcs_count = len(lcs_union) - prev_count
return new_lcs_count, lcs_union
def rouge_l_summary_level(evaluated_sentences, reference_sentences):
"""
Computes ROUGE-L (summary level) of two text collections of sentences.
http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Calculated according to:
R_lcs = SUM(1, u)[LCS<union>(r_i,C)]/m
P_lcs = SUM(1, u)[LCS<union>(r_i,C)]/n
F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
where:
SUM(i,u) = SUM from i through u
u = number of sentences in reference summary
C = Candidate summary made up of v sentences
m = number of words in reference summary
n = number of words in candidate summary
Args:
evaluated_sentences: The sentences that have been picked by the
summarizer
reference_sentence: One of the sentences in the reference summaries
Returns:
A float: F_lcs
Raises:
ValueError: raises exception if a param has len <= 0
"""
if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
raise ValueError("Collections must contain at least 1 sentence.")
# total number of words in reference sentences
m = len(set(_split_into_words(reference_sentences)))
# total number of words in evaluated sentences
n = len(set(_split_into_words(evaluated_sentences)))
# print("m,n %d %d" % (m, n))
union_lcs_sum_across_all_references = 0
union = set()
for ref_s in reference_sentences:
lcs_count, union = _union_lcs(evaluated_sentences,
ref_s,
prev_union=union)
union_lcs_sum_across_all_references += lcs_count
llcs = union_lcs_sum_across_all_references
r_lcs = llcs / m
p_lcs = llcs / n
beta = p_lcs / (r_lcs + 1e-12)
num = (1 + (beta**2)) * r_lcs * p_lcs
denom = r_lcs + ((beta**2) * p_lcs)
f_lcs = num / (denom + 1e-12)
return {"f": f_lcs, "p": p_lcs, "r": r_lcs}