Text summarization

Difference between Abstract & Extract

• Abstract Summarization: Abstract summarization involves generating a concise summary of the input document in a way that it may not use the exact words or sentences from the original text. Instead, it aims to capture the main ideas and concepts in a more abstract and coherent manner. This approach often requires natural language generation techniques and can be more flexible in conveying the essence of the source text.

• Extractive Summarization: Extractive summarization, on the other hand, focuses on selecting and extracting the most important sentences or phrases directly from the source document to form the summary. These selected segments are typically taken verbatim from the original text. Extractive summarization is simpler and doesn't involve generating new sentences but may sometimes result in less coherent summaries.

More information about:

In this project, three models were trained and fine-tuned for abstractive summarization, with the Google/mT5-small model currently deployed. The model, trained on 'Xsum' in English and 'Mlsum' in French, excels in generating high-quality abstractive summaries. Notably, it emphasizes multilingual summarization, capable of summarizing content in both English and French. Evaluation metrics, such as Rouge scores, ensure the model consistently delivers precise and coherent summaries.

Technical information:

• Google/mT5-small: ~ 300 Million parameters

• Framework: PyTorch/HuggingFace

• Machine: GPU - NVIDIA RTX 4090

• Epochs: ~10 epochs | 22 hours

• Dataset: "Xsum" English & "MLsum" French

PS: The model can summarize in English and in French.