QAEval Experiments

This repository will contain the code to reproduce the experiments from Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary. If you want to use QAEval, the easiest way is through its implementation in SacreROUGE, which is far simpler and easier to use than this repository.

Environments

The experiments run several different evaluation metrics and deep learning libraries. Each requires a specific set of dependencies, so we have a conda environment for each one. The envs directory contains the output from conda env export <environment-name> for the environments we used in our experiments. Before running each of the scripts, you need to set environment variables in setup-env.sh that name each of the environments. Edit that file to point to your environment names, then run source setup-env.sh before running the experiment scripts.

Here is a description of each one:

• envs/qaeval-environment.yml: The environment for interactions with sacrerouge, question generation, and MoverScore. It corresponds to QAEVAL_ENV and MOVERSCORE_ENV in setup-env.sh
• envs/transformers-environment.yml: The environment for the QA model of QAEval. It corresponds to TRANSFORMERS_ENV in setup-env.sh
• envs/apes-environment: The environment to run APES. It corresponds to APES_ENV in setup-env.sh
• envs/pyreval-environment: The environment to run PyrEval. It corresponds to PYREVAL_ENV in setup-env.sh

Each of the environments should be able to be recreated with conda create -f <filename>. Further, several of them require installing Spacy model files with pip commands, as follows:

• envs/qaeval-environment.yml: pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz
• envs/transformers-environment.yml: pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
• envs/apes-environment.yml: pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz As far as we know, these can't be included in the environment files, so it needs to be done manually after creating the environments.

Further, each of the scripts must initialize conda before they run using source ~/miniconda3/etc/profile.d/conda.sh. If that is not the location of your conda setup script, you will need to replace it in all of the scripts you want to run.

The QAEVAL_ENV must have ROUGE and PyrEval setup:

sacrerouge setup-metric rouge
sacrerouge setup-metric pyreval

The MOVERSCORE_ENV must have MoverScore setup:

sacrerouge setup-metric moverscore

The APES_ENV must have APES setup:

sacrerouge setup-metric apes

Data Dependencies

Some of the experiments require access to the TAC 2008 and 2009 datasets, which we cannot provide due to license restrictions. If you have access to them, first set them up using SacreROUGE (see here and here). Then copy the following files over to the data directory of this repo:

• {tac2008,tac2009}/task1.A.summaries.jsonl -> data/{tac2008,tac2009}/summaries.jsonl
• {tac2008,tac2009}/task1.A.metrics.jsonl -> data/{tac2008,tac2009}/metrics.jsonl
• {tac2008,tac2009}/task1.A.pyramids.jsonl -> data/{tac2008,tac2009}/pyramids.jsonl
• {tac2008,tac2009}/task1.A.pyramid-annotations.jsonl -> data/{tac2008,tac2009}/pyramid-annotations.jsonl

The Fabbri (2020) data dependencies can be setup using SacreROUGE. See here. We have also included the data in this repo since it is publicly available (data/fabbri2020).

Model Dependencies

Running QAEval requires a pre-trained question generation and question answering models. The code to retrain each of those models can be found here and here.

The models are available for download here:

• Question generation
• Question answering

The generation model should be downloaded to models/generation/model/model.tar.gz. The answering model should be downloaded and unzipped in the models/answering/model/ directory. The files in that directory should look like:

ls -l models/answering/model
> config.json
> pytorch_model.bin
> special_tokens_map.json
> tokenizer_config.json
> training_args.bin
> vocab.txt

TAC 2008 & 2009 Correlations

To calculate the correlations of the metrics to human judgments on the TAC 2008 and 2009 datasets (Table 4), see experiments/end-to-end. There is a subdirectory for each metric with a script that will score summaries and calculate the correlation to the ground-truth judgments.

The required input files to run the scripts are:

• data/{tac2008,tac2009}/summaries.jsonl
• data/{tac2008,tac2009}/metrics.jsonl

Fabbri (2020) Correlations

We used the implementations of the metrics in SacreROUGE to calculate the Fabbri (2020) correlations (Table 5). For sample, see here for calculating QAEval's correlations on Fabbri (2020).

Answer Selection Results

See here to reproduce the results about mapping questions to SCUs (Table 1).

Question Generation Results

See here to reproduce the results about human vs system-generated questions (Figure 3).

Question Answering Results

See here to reproduce the results about the QA performance/metric correlations on the labeled TAC 2008 data (Tables 2 and 3). See here to reproduce the results about the QA performance/metric correlations on the labeled Fabbri (2020) data (Tables 2 and 3).

Number of References Learning Curve Results

See here to reproduce the learning curve in the appendix that measures the impact of additional reference summaries (Figure 6).