Pointer-Generator Networks have emerged as a sophisticated solution for generating concise summaries by pulling relevant content directly from input text. In this article, we’ll break down how you can train a Pointer-Generator network using instructions derived from the implementation referenced in *[Get To The Point: Summarization with Pointer-Generator Networks](https://arxiv.org/abs/1704.04368)*.

Train with Pointer Generation and Coverage Loss Enabled

When you enable both pointer generation and coverage loss, your network will efficiently learn to summarize text. Here are the results after training for 100k iterations with a batch size of 8:

• ROUGE-1:
  • F-score: 0.3907 (CI: 0.3885, 0.3928)
  • Recall: 0.4434 (CI: 0.4410, 0.4460)
  • Precision: 0.3698 (CI: 0.3672, 0.3721)
• ROUGE-2:
  • F-score: 0.1697 (CI: 0.1674, 0.1720)
  • Recall: 0.1920 (CI: 0.1894, 0.1945)
  • Precision: 0.1614 (CI: 0.1590, 0.1636)
• ROUGE-L:
  • F-score: 0.3587 (CI: 0.3565, 0.3608)
  • Recall: 0.4067 (CI: 0.4042, 0.4092)
  • Precision: 0.3397 (CI: 0.3371, 0.3420)

Training with Pointer Generation Enabled

If you choose to enable only pointer generation, here’s what you can expect after 500k iterations (batch size 8):

• ROUGE-1:
  • F-score: 0.3500 (CI: 0.3477, 0.3523)
  • Recall: 0.3718 (CI: 0.3693, 0.3745)
  • Precision: 0.3529 (CI: 0.3501, 0.3555)
• ROUGE-2:
  • F-score: 0.1486 (CI: 0.1465, 0.1508)
  • Recall: 0.1573 (CI: 0.1551, 0.1597)
  • Precision: 0.1506 (CI: 0.1483, 0.1529)
• ROUGE-L:
  • F-score: 0.3202 (CI: 0.3179, 0.3225)
  • Recall: 0.3399 (CI: 0.3374, 0.3426)
  • Precision: 0.3231 (CI: 0.3205, 0.3256)

How to Run Training

To kickstart your training, follow these streamlined steps:

1. First, refer to the data generation instructions available at this GitHub repository.
2. Execute start_train.sh. You may need to adjust certain paths and parameters within data_util/config.py.
3. For the different processes:
   • For training, use start_train.sh.
   • To decode, utilize start_decode.sh.
   • For evaluation, run run_eval.sh.

Note: During decoding, the beam search batch should contain only one example replicated to match the batch size as indicated in this code reference.

Also, the implementation has been tested using PyTorch 0.4 with Python 2.7. Make sure to set up pyrouge to retrieve the ROUGE score.

Papers Using This Code

Over time, various research papers have utilized this code for their summarization tasks. Here are a few notable mentions:

• Automatic Program Synthesis of Long Programs with a Learned Garbage Collector (NeuroIPS 2018)
• Automatic Fact-guided Sentence Modification (AAAI 2020)
• Resurrecting Submodularity in Neural Abstractive Summarization
• StructSum: Summarization via Structured Representations (EACL 2021)
• Concept Pointer Network for Abstractive Summarization (EMNLP 2019)
• PaddlePaddle version
• VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization (INLG 2019)
• Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
• Abstractive Spoken Document Summarization using Hierarchical Model with Multi-stage Attention Diversity Optimization (INTERSPEECH 2020)
• Nutribullets Hybrid: Multi-document Health Summarization (NAACL 2021)

Troubleshooting

If you encounter issues during training, ensure that:

• The paths set in scripts are correct.
• The required packages are installed correctly.
• Framework version compatibility matches as described.

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