Package: content-ranker
Multi-Class Content Ranking using Seq-to-Seq ModelsThis repository contains code for a multi-class HTML content ranking model using BiLSTM-CRF, as described in the paper "A High-Speed Multi-Class Content Ranking using Seq-to-Seq Models".OverviewThe model classifies HTML content into 6 classes indicating importance:TitleMain contentRelated partsOther topicsHeaders and footersUseless partsThe model uses only structural HTML features and is language agnostic.AbstractContent Ranking is an important task in many applications, such as documents ranking, text retrieval, text summarization and other natural language processing (NLP) procedures. Existing approaches define content ranking as a binary classification boilerplate removal, in which a text is either boilerplate or main-content. While boilerplate removal provides a basic mean for text ranking, in many applications, more importance levels are required to distinguish between different parts of an HTML document.Based on a new dataset of segmented and labeled HTML documents, in this paper we introduce a high-speed multi-class sequence-to-sequence text classification model for determining the importance level of a text within six levels. As we use only HTML’s structural features in the training process, our model is language-agnostic and also it can be fine-tuned on a small dataset of web pages of other languages.The involved feature extraction uses an optimized algorithm in terms of speed, which makes the end-to-end inference on an html page in about and less than 25ms, which is a huge benefit in terms of scalability of the model. Besides, our model achieves the 83 percent score in accuracy and 74 percent score in average 6 classes f1 score, which is a prominent result.DatasetThe dataset used for this project can be found atZDA Dataset. It contains 1602 labeled HTML documents across 6 classes:TitleMain contentRelated partsOther topicsHeaders and footersUseless partsResultsThe model achieves 83% accuracy and 74% average F1 score on the test set.Confusion MatrixThe confusion matrix shows the model confuses classes with adjacent importance levels:Example OutputThe model generalizes well to new HTML pages:UsageSee theexamplesfor scripts showing end-to-end usage.Basic usage:fromcontent_rankerimportpredictmodel=predict.load_model("path/to/model.pt")html_string=load_html(...)features=extract_features(html_string)tags=model.predict(features)Repository Structure.
├── Dockerfile
├── examples/               - Example scripts showing usage
├── main.py                 - Main entry point  
├── pyproject.toml          - pypi config
├── README.md
├── requirements.txt        - Python requirements
├── src/                    - Source code
│   └── content_ranker/     - Package containing model code
│       ├── features/       - Feature extraction  
│       ├── models/         - PyTorch model definitions
│       ├── resources/      - Resources like pretrained models
│       └── utils/          - Utility functions
└── tests/                  - TestsCitationPlease cite the paper if you use this repository:@article{ashourisefat-raziei-2023-content-ranker,
  title={A High-Speed Multi-Class Content Ranking using Seq-to-Seq Models},
  author={AshouriSefat, Soroush and Kazemi, Reza and Alikhany, Morteza and Raziei, Mohammad and Amini, Arash},
  journal={IEEE Access},
  volume={9},
  pages={XXXXX--XXXXX},
  year={2023},
  publisher={IEEE}
}ContactFor any questions, feel free to open an issue or contact us atsoroush_ashourisefat@ee.sharif.eduormohammadraziei1375@gmail.com.