README.md

Text summarization

Repository created as part of a collaboration between students from Wrocław University of Technology and Event Registry. The task is to create a summary of several texts, assuming that the texts come from the same event.

For full use of scripts, yous should copy the config file from config/config to config/config.local and complete with your key.

[eventRegistry]
    apiKey = yourapiKey

Setup env

$ python3.9 -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements.txt

Demo

A prototype has been created with the help of the Streamlit library, to run the application you need to run:

streamlit run experiments\scripts\streamlit_app.py

You can use the APIs provided by Event Registry to slap together 4 similar texts. All needed information you can find under the link: https://github.com/EventRegistry/event-registry-python/wiki/Get-event-information. We use four the most important articles in event (with sortBy: sourceImportance). Remember to use the key in the config/config.local file. #

If you don't want to use the api, and you are sure that the 4 texts are similar/refer to the same event you can simply paste them in the spaces provided. You can find example texts in data_input_streamlit_example.

Remember to use the key in the config/config.local file. Tip: To find event_id (eventUri) you can use implemented by us script: experiment\scripts\download_data.py. For example, you can look for some topic using recently_added).

From each text you can get short summaries of paragraphs (or sections, depending on how the text is divided - this is a hyperparameter).

It is possible to summarize a single text with the help of two models:

• anikethdev/t5-summarizer-for-news

• bert2bert_cnn_daily_mail

You can then summarize all 4 texts, we suggest two options:

• summary, which is formed from the concatenation of the original texts (original selected texts):

• bert:

or

• summary, which is formed from the concatenation of the summaries of texts (summary from selected texts):

• t5:

Concatenated text includes 128 tokens from each text (article/article's summary).

From our observations (looking at the rouge and cosine similarity metric), generative models work better than bert, and do a bit better at creating a summary based on the original texts (that is, we accept the loss of much of the text with the idea that the most important is at the top of the article).

Scripts

We have implemented several scripts that can help analyze the problem. They can be found in experiment\scripts. Configuration of the models and datasets used by model is implemented in the experiments\config\model.yaml --output_dir.

For example to create texts summary (using model and dataset defined by summarizer_for_news in experiments/config/models.yaml) run:

$ python experiment\scripts\text_summary.py --hparams_path experiments/config/models.yaml --model summarizer_for_news  --output_dir  output

since this configuration is default configuration you can simply run:

$ python experiment\scripts\text_summary.py

Used methods

Bullet point summarization

This methodology involves segmenting the articles into bullet points in order to condense their content. The pipeline can be broken down into two primary steps:

1. Segmentation of the text into single paragraphs
2. Summarization of each paragraph into a concise bullet point.

Text segmentation into paragraphs can be achieved through either semantic or lexical means. Semantic splitting, while ideal for summarization, can be challenging to implement. With the use of the EventRegistry API, article division can be accomplished through the inclusion of an empty line character (\n\n) or HTML tags such as <p> when scrapping the data. More advanced machine learning techniques require further research and testing, particularly in terms of efficiency.

Lexical splitting, on the other hand, is a more simplistic approach, which can be done with the division of the text into n equal chunks of words. A superior alternative would be to divide the text into sentences, although this method necessitates greater effort than separating the text by dots.

Existing pretrained models, such as snrspeaks/t5-one-line-summary or anikethdev/t5-summarizer-for-news, can be efficiently utilized for the summarization component. For multi-lingual applications, the mT5 model, such as ctu-aic/mt5-base-multilingual-summarization-multilarge-cs, should be considered but will need to undergo testing.

Split strategies

To provide a summarized version of the text, three different splitting strategies were employed:

1. The first strategy, known as the "empty line split," is based on identifying special characters like \n\n or <p> in the articles that indicate separate paragraphs.
2. The second strategy, called the "sentence split," involves dividing the article into roughly equal chunks based on whole sentences.
3. Finally, the "equal split" strategy involves dividing the article into approximately equal-sized chunks of n length.