Multi-DYLE

This is code for the ACL 2023 paper ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent

Model named Multi-DYLE is extended-version of DYLE(https://github.com/Yale-LILY/DYLE) for "DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization," which distributed under MIT License Copyright (c) 2021 Yale-LILY.

Build dataset

You can build dataset by executing python file.

# or you can specify your own path
python data/convert.py \
    --qmsum QMSUM_ROOT_DIRECTORY \
    --dialogue_act ACL2018_ABSSUMM_ROOT_DIRECTORY \
    --save_dir EXPLAIMEETSUM_DIRECTORY

ExplainMeetSum

ExplainMeetSum data is extended-version of QMSum dataset. To annotate evidence sentence by sentence, we had to split sentences as correctly as we can. Below are our methods how to split sentences

1. meeting transcripts
   • Committee: use nltk.sent_tokenize()
   • Academic(ICSI), Product(Ami): use dialogue act files in ACL2018_AbsSumm
2. answers in query_list (i.e. summary)
   • use nltk.sent_tokenize() and merge sentences that splited wrongly (if you want to know, refer to sentence_split.py)
   • splited answers are already stored in data/SummaryEvidence

QMSum

ExplainMeetSum data should also contain meeting_transcripts which doesn't exist in data/SummaryEvidence.
So, you need original QMSum/ dataset.

ACL2018_AbsSumm

We splited meeting_transcripts of ICSI and Ami dataset in QMSum by using dialogue act files.
So, you need acl2018_abssumm/ for dialogue act files.

Dependency

Install dependencies via:

conda create -n multidyle python=3.9.6
conda activate multidyle
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install nltk==3.6.2 pyrouge==0.1.3 transformers==4.8.1 rouge==1.0.0 datasets==1.11.0

Training

sh train_multi_dyle.sh

You can see other results by editing config.py

Evaluation

sh test_multi_dyle.sh