FactSumm: Factual Consistency Scorer for Abstractive Summarization

Last update: Jan 09, 2023

Overview

FactSumm: Factual Consistency Scorer for Abstractive Summarization

FactSumm is a toolkit that scores Factualy Consistency for Abstract Summarization

Without fine-tuning, you can simply apply a variety of downstream tasks to both the source article and the generated abstractive summary

For example, by extracting fact triples from source articles and generated summaries, we can verify that generated summaries correctly reflect source-based facts ( See image above )

As you can guess, this PoC-ish project uses a lot of pre-trained modules that require super-duper computing resources

So don't blame me, just take it as a concept project 👀

Installation

FactSumm requires Java to be installed in your environment to use Stanford OpenIE. With Java and Python 3, you can install factsumm simply using pip:

pip install factsumm

Or you can install FactSumm from source repository:

git clone https://github.com/huffon/factsumm
cd factsumm
pip install .

Usage

>>> from factsumm import FactSumm
>>> factsumm = FactSumm()
>>> article = "Lionel Andrés Messi (born 24 June 1987) is an Argentine professional footballer who plays as a forward and captains both Spanish club Barcelona and the Argentina national team. Often considered as the best player in the world and widely regarded as one of the greatest players of all time, Messi has won a record six Ballon d'Or awards, a record six European Golden Shoes, and in 2020 was named to the Ballon d'Or Dream Team."
>>> summary = "Lionel Andrés Messi (born 24 Aug 1997) is an Spanish professional footballer who plays as a forward and captains both Spanish club Barcelona and the Spanish national team."
>>> factsumm(article, summary, verbose=True)
SOURCE Entities
1: [('Lionel Andrés Messi', 'PERSON'), ('24 June 1987', 'DATE'), ('Argentine', 'NORP'), ('Spanish', 'NORP'), ('Barcelona',
'GPE'), ('Argentina', 'GPE')]
2: [('one', 'CARDINAL'), ('Messi', 'PERSON'), ('six', 'CARDINAL'), ('European Golden Shoes', 'WORK_OF_ART'), ('2020', 'DATE'),
("the Ballon d'Or Dream Team", 'ORG')]

SUMMARY Entities
1: [('Lionel Andrés Messi', 'PERSON'), ('24 Aug 1997', 'DATE'), ('Spanish', 'NORP'), ('Barcelona', 'ORG')]

SOURCE Facts
('Lionel Andrés Messi', 'per:origin', 'Argentine')
('Spanish', 'per:date_of_birth', '24 June 1987')
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Lionel Andrés Messi', 'per:date_of_birth', '24 June 1987')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

SUMMARY Facts
('Lionel Andrés Messi', 'per:origin', 'Spanish')
('Lionel Andrés Messi', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

COMMON Facts
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

DIFF Facts
('Lionel Andrés Messi', 'per:origin', 'Spanish')
('Lionel Andrés Messi', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'per:date_of_birth', '24 Aug 1997')

Fact Score: 0.5714285714285714

Answers based on SOURCE (Questions are generated from Summary)
[Q] Who is the captain of the Spanish national team?    [Pred] <unanswerable>
[Q] When was Lionel Andrés Messi born?  [Pred] 24 June 1987
[Q] Lionel Andrés Messi is a professional footballer of what nationality?       [Pred] Argentine
[Q] Lionel Messi is a captain of which Spanish club?    [Pred] Barcelona

Answers based on SUMMARY (Questions are generated from Summary)
[Q] Who is the captain of the Spanish national team?    [Pred] Lionel Andrés Messi
[Q] When was Lionel Andrés Messi born?  [Pred] 24 Aug 1997
[Q] Lionel Andrés Messi is a professional footballer of what nationality?       [Pred] Spanish
[Q] Lionel Messi is a captain of which Spanish club?    [Pred] Barcelona

QAGS Score: 0.3333333333333333

SOURCE Triples
('Messi', 'is', 'Argentine')
('Messi', 'is', 'professional')

SUMMARY Triples
('Messi', 'is', 'Spanish')
('Messi', 'is', 'professional')

Triple Score: 0.5

Avg. ROUGE-1: 0.4415584415584415
Avg. ROUGE-2: 0.3287671232876712
Avg. ROUGE-L: 0.4415584415584415

Sub-modules

From here, you can find various way to score Factual Consistency level with Unsupervised methods

Triple-based Module ( closed-scheme )

>>> from factsumm import FactSumm
>>> factsumm = FactSumm()
>>> factsumm.extract_facts(article, summary, verbose=True)
SOURCE Entities
1: [('Lionel Andrés Messi', 'PERSON'), ('24 June 1987', 'DATE'), ('Argentine', 'NORP'), ('Spanish', 'NORP'), ('Barcelona',
'GPE'), ('Argentina', 'GPE')]
2: [('one', 'CARDINAL'), ('Messi', 'PERSON'), ('six', 'CARDINAL'), ('European Golden Shoes', 'WORK_OF_ART'), ('2020', 'DATE'),
("the Ballon d'Or Dream Team", 'ORG')]

SUMMARY Entities
1: [('Lionel Andrés Messi', 'PERSON'), ('24 Aug 1997', 'DATE'), ('Spanish', 'NORP'), ('Barcelona', 'ORG')]

SOURCE Facts
('Lionel Andrés Messi', 'per:origin', 'Argentine')
('Spanish', 'per:date_of_birth', '24 June 1987')
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Lionel Andrés Messi', 'per:date_of_birth', '24 June 1987')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

SUMMARY Facts
('Lionel Andrés Messi', 'per:origin', 'Spanish')
('Lionel Andrés Messi', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

COMMON Facts
('Spanish', 'org:top_members/employees', 'Lionel Andrés Messi')
('Spanish', 'org:members', 'Barcelona')
('Lionel Andrés Messi', 'per:employee_of', 'Barcelona')
('Barcelona', 'org:top_members/employees', 'Lionel Andrés Messi')

DIFF Facts
('Lionel Andrés Messi', 'per:origin', 'Spanish')
('Lionel Andrés Messi', 'per:date_of_birth', '24 Aug 1997')
('Spanish', 'per:date_of_birth', '24 Aug 1997')

Fact Score: 0.5714285714285714

The triple-based module counts the overlap of fact triples between the generated summary and the source document.

QA-based Module

If you ask questions about the summary and the source document, you will get a similar answer if the summary realistically matches the source document

>>> from factsumm import FactSumm
>>> factsumm = FactSumm()
>>> factsumm.extract_qas(article, summary, verbose=True)
Answers based on SOURCE (Questions are generated from Summary)
[Q] Who is the captain of the Spanish national team?    [Pred] <unanswerable>
[Q] When was Lionel Andrés Messi born?  [Pred] 24 June 1987
[Q] Lionel Andrés Messi is a professional footballer of what nationality?       [Pred] Argentine
[Q] Lionel Messi is a captain of which Spanish club?    [Pred] Barcelona

Answers based on SUMMARY (Questions are generated from Summary)
[Q] Who is the captain of the Spanish national team?    [Pred] Lionel Andrés Messi
[Q] When was Lionel Andrés Messi born?  [Pred] 24 Aug 1997
[Q] Lionel Andrés Messi is a professional footballer of what nationality?       [Pred] Spanish
[Q] Lionel Messi is a captain of which Spanish club?    [Pred] Barcelona

QAGS Score: 0.3333333333333333

OpenIE-based Module ( open-scheme )

>>> from factsumm import FactSumm
>>> factsumm = FactSumm()
>>> factsumm.extract_triples(article, summary, verbose=True)
SOURCE Triples
('Messi', 'is', 'Argentine')
('Messi', 'is', 'professional')

SUMMARY Triples
('Messi', 'is', 'Spanish')
('Messi', 'is', 'professional')

Triple Score: 0.5

Stanford OpenIE can extract relationships from raw strings. But it's important to note that it's based on the open scheme, not the closed scheme (like Triple-based Module).

For example, from "Obama was born in Hawaii", OpenIE extracts (Obama, born in Hawaii). However, from "Hawaii is the birthplace of Obama", it extracts (Hawaii, is the birthplace of, Obama). In common sense, the triples extracted from the two sentences should be identical, but OpenIE can't recognize that they are the same since it is based on an open scheme.

So the score for this module may be unstable.

ROUGE-based Module

>>> from factsumm import FactSumm
>>> factsumm = FactSumm()
>>> factsumm.calculate_rouge(article, summary)
Avg. ROUGE-1: 0.4415584415584415
Avg. ROUGE-2: 0.3287671232876712
Avg. ROUGE-L: 0.4415584415584415

Simple but effective word-level overlap ROUGE score

Citation

If you apply this library to any project, please cite:

@misc{factsumm,
  author       = {Heo, Hoon},
  title        = {FactSumm: Factual Consistency Scorer for Abstractive Summarization},
  howpublished = {\url{https://github.com/Huffon/factsumm}},
  year         = {2021},
}

References

You might also like...

Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

Summarization, translation, Q&A, text generation and more at blazing speed using a T5 version implemented in ONNX. This package is still in alpha stag

137 Feb 1, 2021

Comments

BUG: AttributeError: 'str' object has no attribute 'generate'

when I use the example in README to gain qags score, there has a problem：

AttributeError Traceback (most recent call last) in () ----> 1 factsumm.extract_qas(article, summary, verbose=True)

~/Desktop/factsumm-master/factsumm/factsumm.py in extract_qas(self, source, summary, source_ents, summary_ents, verbose, device) 292 summary_ents = self.ner(summary_lines) 293 --> 294 summary_qas = self.qg(summary_lines, summary_ents) 295 296 source_answers = self.qa(source, summary_qas)

~/Desktop/factsumm-master/factsumm/utils/module_question.py in generate_question(sentences, total_entities) 55 ).to(device) 56 ---> 57 outputs = model.generate(**tokens, max_length=64) 58 59 question = tokenizer.decode(outputs[0])

AttributeError: 'str' object has no attribute 'generate'

hope you can help me to solve this problem. Thanks!!

opened by victory-h 0
IndexError: index out of range in self

In example, when I extend the length of the article and summary , I get this error.

/opt/anaconda3/envs/LDA0115/lib/python3.6/site-packages/torch/nn/modules/sparse.py in forward(self, input) 124 return F.embedding( 125 input, self.weight, self.padding_idx, self.max_norm, --> 126 self.norm_type, self.scale_grad_by_freq, self.sparse) 127 128 def extra_repr(self) -> str:

/opt/anaconda3/envs/LDA0115/lib/python3.6/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse) 1850 # remove once script supports set_grad_enabled 1851 no_grad_embedding_renorm(weight, input, max_norm, norm_type) -> 1852 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) 1853 1854

IndexError: index out of range in self

opened by victory-h 0
Hit Error while using this toolkits

Loading Named Entity Recognition Pipeline... Loading Relation Extraction Pipeline... Fact Score: 0.5714285714285714 Loading Question Generation Pipeline... Loading Question Answering Pipeline... Traceback (most recent call last): File "testcase.py", line 5, in print(factsumm(article, summary, verbose=False)) File "/usr/local/lib/python3.8/dist-packages/factsumm/init.py", line 366, in call qags_score = self.extract_qas( File "/usr/local/lib/python3.8/dist-packages/factsumm/init.py", line 263, in extract_qas source_answers = self.qa(source, summary_qas) File "/usr/local/lib/python3.8/dist-packages/factsumm/utils/level_sentence.py", line 100, in answer_question pred = qa( File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/question_answering.py", line 248, in call return super().call(examples[0], **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py", line 915, in call return self.run_single(inputs, preprocess_params, forward_params, postprocess_params) File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py", line 923, in run_single outputs = self.postprocess(model_outputs, **postprocess_params) File "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/question_answering.py", line 409, in postprocess min_null_score = min(min_null_score, (start_[0] * end_[0]).item()) ValueError: can only convert an array of size 1 to a Python scalar

while using provided example in README, I meet the Error above ( I use pip install to install this packet and create the python file, copy the example code and run ) pip uninstall and pip reinstall doesn`t help QAQ any suggestion are greatly appreciated!

opened by Ricardokevins 0

Releases(0.1.2)

0.1.2(May 13, 2021)

Update BERTScore based Module (See Sec 4.1 from https://arxiv.org/pdf/2005.03754.pdf)

>>> factsumm = FactSumm()
>>> factsumm.calculate_bert_score(article, summary)
BERTScore Score
Precision: 0.9151781797409058
Recall: 0.9141832590103149
F1: 0.9150083661079407

Source code(tar.gz)
Source code(zip)

0.1.1(May 12, 2021)
Currently FactSumm supports the following methods:

NER + RE based Triple Module

QG + QA based Module

OpenIE based Triple Module

ROUGE based Module

Source code(tar.gz)
Source code(zip)

FactSumm: Factual Consistency Scorer for Abstractive Summarization

Related tags

Overview

FactSumm: Factual Consistency Scorer for Abstractive Summarization

Installation

Usage

Sub-modules

Triple-based Module ( closed-scheme )

QA-based Module

OpenIE-based Module ( open-scheme )

ROUGE-based Module

Citation

References

You might also like...

Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

Package for controllable summarization

The guide to tackle with the Text Summarization

code for modular summarization work published in ACL2021 by Krishna et al

code for modular summarization work published in ACL2021 by Krishna et al

Codes for processing meeting summarization datasets AMI and ICSI.

SummerTime - Text Summarization Toolkit for Non-experts

Korean extractive summarization. 2021 AI 텍스트 요약 온라인 해커톤 화성갈끄니까팀 코드

Deploying a Text Summarization NLP use case on Docker Container Utilizing Nvidia GPU

Comments

BUG: AttributeError: 'str' object has no attribute 'generate'

when I use the example in README to gain qags score, there has a problem：

IndexError: index out of range in self

Hit Error while using this toolkits

Releases(0.1.2)

0.1.2(May 13, 2021)

0.1.1(May 12, 2021)

Owner

devfon

a chinese segment base on crf

official ( API ) for the zAmericanEnglish app in [ Google play ] and [ App store ]

Natural Language Processing Best Practices & Examples

The projects lets you extract glossary words and their definitions from a given piece of text automatically using NLP techniques

📜 GPT-2 Rhyming Limerick and Haiku models using data augmentation

Bnagla hand written document digiiztion

The entmax mapping and its loss, a family of sparse softmax alternatives.

Snowball compiler and stemming algorithms

STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.

Semantic search for quotes.

A BERT-based reverse dictionary of Korean proverbs

nlp基础任务

An official implementation for "CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval"

NLPretext packages in a unique library all the text preprocessing functions you need to ease your NLP project.

Gpt2-WebAPI - The objective of this API is to provide the 3 best possible responses to sentences that the user would input via http GET request as a parameter

[EMNLP 2021] Mirror-BERT: Converting Pretrained Language Models to universal text encoders without labels.

Analyse japanese ebooks using MeCab to determine the difficulty level for japanese learners

Pretty-doc - Composable text objects with python

One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.

ACL22 paper: Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost