extractnet 2.0.7
Extract the main article content (and optionally comments) from a web page
License: MIT
Keywords: automatic content extraction,web page dechroming,HTML parsing


ExtractNet
=======
[![PyPI version](https://badge.fury.io/py/extractnet.svg)](https://badge.fury.io/py/extractnet) [![codecov](https://codecov.io/gh/currentsapi/extractnet/branch/master/graph/badge.svg?token=0WIGWGFN8H)](https://codecov.io/gh/currentsapi/extractnet) 


Based on the popular content extraction package [Dragnet](https://github.com/dragnet-org/dragnet/), ExtractNet extend the machine learning approach to extract other attributes such as date, author and keywords from news article. 

![demo code](docs/source/_images/showcase.svg)


**Example code:**


Simply use the following command to install the latest released version:

```bash
pip install extractnet
```

Start extract content and other meta data passing the result html to function

```python
import requests
from extractnet import Extractor

raw_html = requests.get('https://currentsapi.services/en/blog/2019/03/27/python-microframework-benchmark/.html').text
results = Extractor().extract(raw_html)
```


## Why don't just use existing rule-base extraction method:

We discover some webpage doesn't provide the real author name but simply populate the author tag with a default value.

For example [ltn.com.tw](https://news.ltn.com.tw), [udn.com](https://udn.com/news/story/7205/5037493) always populate the same author value for each news article while the real author can only be found within the content.

![Our machine learnig first approach extract correct fields just like human reading a website](docs/source/_images/extraction-example.jpg)

ExtractNet uses machine learning approach to extract these relevant data through visible section of the webpage just like a human.

![ExtractNet pipeline](docs/source/_images/extractnet-pipeline.jpg)



## What ExtractNet is and isn't

* ExtractNet is a platform to extract any interesting attributes from any webpage, not just limited to content based article.

* The core of ExtractNet aims to convert unstructured webpage to structured data without relying hand crafted rules

* ExtractNet do not support boilerplate content extraction

* ExtractNet allows user to add custom pipelines that returns additional data through a list of [callbacks function](https://github.com/currentsapi/extractnet#callbacks)

<br />

# Performance

Results of the body extraction evaluation:

We use the same body extraction benchmark from [article-extraction-benchmark](https://github.com/scrapinghub/article-extraction-benchmark) 


| Model  | Precision  | Recall  | F1  | Accuracy  | Open Source |
|---|---|---|---|---|---|
| AutoExtract |  0.984 ± 0.003 | 0.956 ± 0.010  | 0.970 ± 0.005  | 0.470 ± 0.037   | &#10007; |
| Diffbot  | 0.958 ± 0.009  | 0.944 ± 0.013  | 0.951 ± 0.010  |  0.348 ± 0.035  | &#10007; |
| **ExtractNet**  | 0.922 ± 0.011  |  0.933 ± 0.013 | 0.927 ± 0.010  | 0.160 ± 0.027  | &#10004; |
| boilerpipe  | 0.850 ± 0.016  |  0.870 ± 0.020 | 0.860 ± 0.016  | 0.006 ± 0.006   | &#10004; |
| dragnet  |  0.925 ± 0.012 | 0.889 ± 0.018  | 0.907 ± 0.014  | 0.221 ± 0.030   | &#10004; |
| html-text  | 0.500 ± 0.017  | 0.994 ± 0.001  | 0.665 ± 0.015  |  0.000 ± 0.000  | &#10004; |
| newspaper  |  0.917 ± 0.013 | 0.906 ± 0.017  | 0.912 ± 0.014  | 0.260 ± 0.032   | &#10004; |
| readability  | 0.913 ± 0.014   | 0.931 ± 0.015  | 0.922 ± 0.013  | 0.315 ± 0.034   | &#10004; |
| trafilatura  | 0.930 ± 0.010  | 0.967 ± 0.009  | 0.948 ± 0.008   | 0.243 ± 0.031   | &#10004; |

<br />
Results of author name extraction:

| Model  | F1  |
|---|---|
| **ExtractNet** : fasttext embeddings + CRF |  0.904 ± 0.10  |

<br />

## List of changes from Dragnet

* Underlying classifier is replaced by [Catboost](https://catboost.ai/) instead of [Decision Tree](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) for all attributes extraction for consistency and performance boost.

* Updated CSS features, added text+css latent feature 

* Includes a [CRF](https://en.wikipedia.org/wiki/Conditional_random_field) model that extract names from author block text.

* Trained on 22000+ updated webpages collected in the late 2020, **20 times** of dragnet data.

## GETTING STARTED

### Installing and extraction

```
pip install extractnet
```

```python
import requests
from extractnet import Extractor

raw_html = requests.get('https://apnews.com/article/6e58b5742b36e3de53298cf73fbfdf48').text
results = Extractor().extract(raw_html)
for key, value in results.items():
    print(key)
    print(value)
    print('------------')
```

### Callbacks

ExtractNet also support the ability to add callbacks functions to inject additional features during extraction process

A quick glance of usage : each callbacks will be able to access the raw html string provided during the extraction process. This allows user to extract addtional information such as language detection to the final results

```python
def meta_pre1(raw_html):
    return {'first_value': 0}

def meta_pre2(raw_html):
    return {'first_value': 1, 'second_value': 2}

def find_stock_ticker(raw_html, results):
    matched_ticker = []
    for ticket in re.findall(r'[$][A-Za-z][\S]*', str(results['content'])):
      matched_ticker.append(ticket)
    return {'matched_ticker': matched_ticker}

extract = Extractor(author_prob_threshold=0.1, 
      meta_postprocess=[meta_pre1, meta_pre2], 
      postprocess=[find_stock_ticker])
```

The extracted results will contain **like**, **first_value** and **second_value**. Do note callbacks are executed by the given order ( which means meta_pre1 will be executed first followed by meta_pre2 ), any results passed from the **previous stage will not be overwritten by later stage** 

```python

raw_html = requests.get('https://apnews.com/article/6e58b5742b36e3de53298cf73fbfdf48').text
results = extract(raw_html)

```

In this example the value for first_value will remain 0 even though meta_pre2 also returns first_value=1 because meta_pre2 callbacks already assign first_value as 0. 


# Contributing

We love contributions! Open an issue, or fork/create a pull
request.

## Develop Locally

Since extractnet relies on several C++ modules, before starting to run locally you need to compile them first

Usually what you need would be this command
```
make
```

However, you can try to build it 

## Supress logging error

Setting the level to critical will suppress any logging output 

```python
from extractnet import Extractor
from extractnet.blocks import BlockifyError
logging.getLogger('extractnet').setLevel(logging.CRITICAL)

extractor = Extractor()
```

# More details about the code structure

Coming soon


## Reference

### Content extraction using diverse feature sets

[1] Peters, Matthew E. and D. Lecocq, [*Content extraction using diverse feature sets*](https://dl.acm.org/doi/10.1145/2487788.2487828)

```
@inproceedings{Peters2013ContentEU,
  title={Content extraction using diverse feature sets},
  author={Matthew E. Peters and D. Lecocq},
  booktitle={WWW '13 Companion},
  year={2013}
}
```

### Bag of Tricks for Efficient Text Classification

[2] A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [*Bag of Tricks for Efficient Text Classification*](https://arxiv.org/abs/1607.01759)

```
@article{joulin2016bag,
  title={Bag of Tricks for Efficient Text Classification},
  author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Mikolov, Tomas},
  journal={arXiv preprint arXiv:1607.01759},
  year={2016}
}
```