Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization

Jump to:

• MINT Abstractiveness Score
• Nonlinear Abstractiveness Constraints
• Datasets
• Citation
• Security
• License

This is the repository for the Dreyer et al (2023) paper:

Markus Dreyer, Mengwen Liu, Feng Nan, Sandeep Atluri, Sujith Ravi. 2023. Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization. In Proceedings of EACL Findings.

The repository contains our code and two datasets:

• ConstraintsFact: 10.2k factuality judgements
• ModelsFact: 4.2k factuality judgements

Paper abstract: Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents. In this paper, we analyze the tradeoff between abstractiveness and factuality of generated summaries across multiple datasets and models, using extensive human evaluations of factuality. In our analysis, we visualize the rates of change in factuality as we gradually increase abstractiveness using a decoding constraint, and we observe that, while increased abstractiveness generally leads to a drop in factuality, the rate of factuality decay depends on factors such as the data that the system was trained on. We introduce two datasets with human factuality judgements; one containing 10.2k generated summaries with systematically varied degrees of abstractiveness; the other containing 4.2k summaries from five different summarization models. We propose new factuality metrics that adjust for the degree of abstractiveness, and we use them to compare the abstractiveness-adjusted factuality of previous summarization works, providing baselines for future work.

MINT Abstractiveness Score

Our paper introduces the MINT score to measure the degree of abstractiveness as a percentage score. It is computed based on contiguous and non-contiguous overlap between the input (source) and the generated output text (target).

To install, type:

cd mintscore
pip install ./

The pip command installs a script called mint, which you can call like this:

# Show help text
mint --help

# Score target with respect to the corresponding source (line-by-line results on STDOUT)
mint --source source.txt target.txt

# Score target with respect to the corresponding source (compact results on STDOUT)
mint --compact --source source.txt target.txt

Nonlinear Abstractiveness Constraints

Our paper introduces nonlinear abstractiveness constraints to control the degree of abstractiveness during beam decoding. Using the constraint with different values for the parameter h, you can generate summaries with varying degrees of abstractiveness, as in the figure below.

Python library abstractive_constraints

The nonlinear abstractiveness constraints are implemented as a Python library called abstractive_constraints.

To install, type:

cd abstractive_constraints
pip install ./

The library provides functionality to incrementally build a target sequence, compute matches against source tokens and then score (or, penalize) the matches. It can be used in any beam decoder.

Integration into Fairseq

We provide a reference implementation of using the abstractive_constraints library in the Fairseq decoder.

To run it, first install Fairseq into a parent directory, then apply our diff file fairseq-1e40a48.diff, which inserts calls to the abstractive_constraints library:

cd ~
git clone https://github.com/pytorch/fairseq.git
cd fairseq
git reset --hard 1e40a48
pip install --editable ./
patch -p0 < ~/abstractive-factual-tradeoff/fairseq-1e40a48.diff # from this repo

You can then run inference with a BART model using abstractiveness constraints as follows.

First, download the bart.large.cnn model (see here) and unpack it:

mkdir -p ~/fairseq/models
cd ~/fairseq/models
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz
tar -xzf bart.large.tar.gz

Then, use our misc/bart_decode.py script, which takes an --extractive-penalty as a command line option to control abstractiveness:

python ${HOME}/abstractive-factual-tradeoff/misc/bart_decode.py \
  --extractive-penalty 'log_exp(2,2.402244)' \
  --gpus=4 --batch-size=4
  --min-len=55 --max-len-b=140 \
  --model=${HOME}/fairseq/models/bart.large.cnn/model.pt \
  --task=${HOME}/fairseq/models/bart.large.cnn \
  --output output.txt \
  input.txt

The log_exp(k,c) stands for the function x**k/c**k, where x is the length of an extractive fragment. In the example command above, we use an exponent of k=2 (see Footnote 3 in our paper) and c=2.402244. This corresponds to the (negative) log of Equation 1 in our paper with h=2, see Appendix B.

Datasets

We release two factuality datasets: ConstraintsFact and ModelsFact, as described in Section 3.1 of our paper.

ConstraintsFact

This dataset contains 10.2k human factuality judgements:

• 600 x 4 summaries of CNN/DM input generated by BART-Large trained on CNN/DM (using 4 different abstractiveness decoding constraints)
• 600 x 5 summaries of XSum input generated by BART-Large trained on XSum
• 600 x 4 summaries of Multi-News input (truncated to 800 input tokens) generated by BART-Large trained on Multi-News (training data truncated to 800 input tokens)
• 600 x 4 summaries of Multi-News input (truncated to 500 tokens) generated by BART-Large trained on Multi-News (training data truncated to 500 tokens -- more aggressive truncation means the model learns to hallucinate more)

For each of the 10.2k summaries, one randomly selected sentence (displayed in context) was annotated for factuality by three annotators, and an aggregated judgement (produced by MACE) has been added.

See details here.

ModelsFact

This dataset contains 4.2k human factuality judgements:

CNN/DM: * 600 summaries generated by BART-Large * 600 summaries generated by BertSum (Liu and Lapata, 2019) * 600 summaries generated by PGConv (See et al., 2017) * 600 summaries generated by BottomUp (Gehrmann et al., 2018) * 600 summaries generated by AbsRL (Chen and Bansal, 2018)

XSum * 600 summaries generated by BART-Large * 600 summaries generated by BertSum

For each of these 4.2k summaries, one randomly selected sentence (displayed in context) was annotated for factuality by three annotators, and an aggregated judgement (produced by MACE) has been added.

See details here.

Citation

@inproceedings{dreyer-etal-2023-tradeoff,
  title = "Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization",
  author = "Dreyer, Markus and Liu, Mengwen and Nan, Feng and Atluri, Sandeep and Ravi, Sujith",
  booktitle = "Findings of the European Association for Computational Linguistics: EACL
2023",
  month = {May},
  year = "2023",
  address = "Dubrovnik, Croatia",
  publisher = "Association for Computational Linguistics",
  url = "https://arxiv.org/abs/2108.02859"
}

Security

See CONTRIBUTING for more information.

License

This project is licensed under the MIT-0 License.