CONTOUR: Penalty and Spotlight Mask for Abstractive Summarization

• Authors: Trang-Phuong N. Nguyen and Nhi-Thao Tran
• Submitted ACIIDS 2021

The main purpose of CONTOUR is to emphasize the most related word containing the important information at each specific time by producing distinctive contours of the word’s potential. In contrast to baselines, which are Pointer Generator Network and SELECTOR, we aim to take advantage of the last predicted word to the input words’ significance.

The proposed Contour is an association of two independent sub-methods: Penalty and Spotlight.

• Penalty is the updated version of Selector that improves the focus areas generation and optimizes the inference time.
• Spotlight generates the spotlight mask, which is used to re-ranking words potential by building an instance context from the last output word.

Datasets

We examined CONTOUR on multiple types of datasets and languages, which are:

• Large-scale CNN/DailyMail for English
• Medium-scale VNTC-Abs for Vietnamese
• Small-scale Livedoor News Corpus for Japanese

Result

Dataset	R-1	R-2	R-L
CNN/DailyMail	41.86	18.80	38.46
VNTC-Abs	26.80	9.50	24.16
Livedoor	31.68	14.83	29.26

How to run

1. Check the data_util/config.py, fill your path to store or load models
2. Prepare the dataset by converting it to chunked bin files by the make_data_files.py
3. Train: python3 train.py --model_name="model_name"
4. Evaluate: python3 eval.py --task=validate --model_name="model_name" --start_from=checkpoint.tar
5. Test: python3 eval.py --task=test --model_name="model_name" --load_model=checkpoint.tar

Citations

@inproceedings{trangphuong-etal-2020-contour,
  author = {Trang-Phuong, N. Nguyen and Nhi-Thao, Tran},
  year = {2020},
  month = {10},
  title = {CONTOUR: Penalty and Spotlight Mask for Abstractive Summarization},
  publisher = "Submitted Asian Conference on Intelligent Information and Database Systems",
}