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RODS

The official repository for ACL 2022 main conference paper: Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions.

Instructions

1. Introduction

We propose two role interaction methods to enhance the role-oriented dialogue summarization task. Two methods include cross attention interaction and decoder self-attention interaction. The cross attention interaction adopts an attention divergence loss to let each role decoder attend to the most useful utterances from other roles. The decoder self-attention interaction adopts the interactive decoding strategy to consider other roles' summaries when generating summaries.

We experiment on two datasets (CSDS and MC), and two baseline methods (PGN and BERTAbs).

2. Necessary Resources

  • CSDS dataset: Please refer to the original repository for downloading the dataset
  • MC dataset: Please refer to the process provided by the original repository. (Due to the website policy, we are unable to directly provide the processed data. However, we provide the splits of urls for train, val and test datasets in data/MC/)
  • Pretrained BERT model: We use the base version of Chinese BERT-wwm, available at here.
  • Tencent embeddings: If you want to train on other Chinese dataset for PGN-based model, you need to extract the pretrained embeddings through tencent embeddings, available at here.

3. Usage

Requirements

  • python == 3.7
  • pytorch == 1.8
  • files2rouge == 2.1.0
  • jieba == 0.42.1
  • numpy == 1.19.1
  • tensorboard == 2.3.0
  • tensorboardx == 2.1
  • cytoolz == 0.11.0
  • nltk == 3.5
  • bert-score == 0.3.6
  • moverscore == 1.0.3
  • transformers == 3.0.0

Instruction for PGN interact

  1. Go to the models/PGN_interact/ directory.

  2. Download the CSDS/MC dataset, and put the data under the folder data/CSDS or data/MC.

  3. If you want to extract the pretrained embeddings, download the tencent embedding and put it under the ../pretrained/ folder, else you could use our provided extracted embeddings and pass this step.

    For MC dataset, the extracted embeddings are a bit large and you could download it through:

    After downloading, please put it under the models/PGN_interact/data_utils/embeddings folder

  4. Run the bash file run_CSDS.sh or run_MC.sh to train and test.

Instruction for BERTAbs interact

  1. Go to the models/BERT_interact/ directory.
  2. Download the CSDS/MC dataset, and put the data under the folder data/CSDS or data/MC.
  3. Download the Chinese BERT-wwm pretrained models, create a new folder named bert_base_chinese/ and put it under the folder ../pretrained/.
  4. Run the bash file run_CSDS.sh or run_MC.sh to train and test.

Evaluation

  1. We put the output of our trained models to the results/ folder. If you have trained your models, you could also put the outputs into the folder.
  2. Run evaluate/evaluate.py to evaluate through automatic metrics. Pay attention to change the file names if you want to test your own output.

Our pretrained Checkpoints

We also provide some checkpoints for PGN-both and BERT-both. You could download them through the following links:

4. Acknowledgement

The reference code of the provided methods are:

We thanks for all these researchers who have made their codes publicly available.

5. Citation

If you want to cite our paper, please use this ACL proceeding bibtex version:

@inproceedings{lin-etal-2022-roles,
    title = "Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions",
    author = "Lin, Haitao  and
      Zhu, Junnan  and
      Xiang, Lu  and
      Zhou, Yu  and
      Zhang, Jiajun  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.182",
    pages = "2545--2558",
    abstract = "Role-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role{'}s content separately and thus are prone to ignore the information from other roles. However, we believe that other roles{'} content could benefit the quality of summaries, such as the omitted information mentioned by other roles. Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization. It adopts cross attention and decoder self-attention interactions to interactively acquire other roles{'} critical information. The cross attention interaction aims to select other roles{'} critical dialogue utterances, while the decoder self-attention interaction aims to obtain key information from other roles{'} summaries. Experimental results have shown that our proposed method significantly outperforms strong baselines on two public role-oriented dialogue summarization datasets. Extensive analyses have demonstrated that other roles{'} content could help generate summaries with more complete semantics and correct topic structures.",
}

If you have any issues, please contact with [email protected]