Better Highlighting: Creating Sub-Sentence Summary Highlights

We provide the source code for the paper "Better Highlighting: Creating Sub-Sentence Summary Highlights", accepted at EMNLP'20. If you find the code useful, please cite the following paper.

@inproceedings{cho-song-li-yu-foroosh-liu:2020,
 Author = {Sangwoo Cho and Kaiqiang Song and Chen Li and Dong Yu and Hassan Foroosh and Fei Liu},
 Title = {Better Highlighting: Creating Sub-Sentence Summary Highlights},
 Booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
 Year = {2020}}

Goal

• Our system seeks to summarize multi-document articles with sub-sentence segments
• The code consists of sub-sentence segment generation, segment importance and similarity score computation, and DPP.

Dependencies

The code is written in Python (v3.6) and Pytorch (v1.4). We suggest the following environment:

• Python (v3.6)
• Pytorch (v1.4)
• Pyrouge
• spaCy
• Huggingface Transformer

Sub-sentence segment generation

• seg_gen.py: generate all segments from sentences with XLNet
  $ python seg_gen.py --dataset 0 --split train --data_start 0 --data_end 1

• seg_filter_subsent.py: filter out segments (generate candidate segments for a summary)
  $ python seg_filter_subsent.py --dataset 0

• draw_fullsent_pos.py: draw positions of original sentences in percent
  $ python draw_fullsent_pos.py

BERT-sim, BERT-imp fine-tuning on CNN/DM

Data generation

• We use the CNN/DM summary dataset, downloaded from HERE (pre-processed CNN/DM summary data file direct link).
  • The data contains a list of candidate summary sentences (the most similiar sentences to the summary) in each article.
• gen_cnndm_pairs.py: generate train/test data for BERT-sim (pair), BERT-imp (pair_leadn)
  • The generated data is balanced.

BERT-sim, BERT-imp train / test

• run_finetune.py: main
• train_finetune.py: trainer
• dataset_cnndm.py: data feeder
• BERT-sim: $ python run_finetune.py --data_type pair --max_seq_len 128
• BERT-imp: $ python run_finetune.py --data_type pair_leadn --max_seq_len 512

BERT-sim, BERT-imp prediction on target dataset

• run_bert_scores.py: BERT similarity and importance score prediction (DUC, TAC)
  $ python run_bert_scores.py --dataset 0 --data_type xlnet --split train --data_start 0 --data_end 1 --gpu_id 0 --batch_size_imp 5 --batch_size_sim 25
• merge_bert_ext.py: merge predicted files into one file and convert *.pkl to *.mat (BERT feature file on CNN is *.h5 and no conversion to *.mat)
  $ python merge_bert_ext.py --dataset 0 --data_type xlnet --split train

Text generation for DPP training

• gen_text_DPP.py: generate texts (.txt, .words, .pos, .Y, .YY, .seg, idf, dict) for DPP training/testing from candidate segments or sentences
  $ python gen_text_DPP.py --dataset 0 --data_type xlnet

Utility files

• read_text_from_data.py: text loader for DUC, TAC
• utils.py: utility functions

DPP training, testing

• under src/DPP directory
• $ bash run.bash run_DPP.m
• run_DPP.m: main file to set parameters and run DPP training/testing
• main_DPP.m: read train/test text and call DPP.m
• DPP.m: set more specific parameters, assign features, DPP train/test

System summary

We provide our best system summaries of DUC-04 and TAC-11 (/summary_results). We do not provide DUC and TAC dataset due to license. Please download DUC 03/04 and TAC 08/09/10/11 dataset with your request and approval.

License

This project is licensed under the BSD License - see the LICENSE.md file for details.