Summarization

We are excited to announce the release of version 1.0 of our summarization model, a compact 128 million parameter model designed for effective text summarization. To ensure that the model's summaries are accurate and meaningful, it' essential to focus on understanding the context of the input text, rather than relying on prior knowledge or assumptions. This allows the model to generate summaries that are grounded in the specific text being analyzed.

In practice, this means that the model should::

• Analyze the structure and organization of the text
• Identify key themes and relationships between ideas
• Extract main points and central arguments directly from the text

Critically, the model should avoid "reading between the lines" or making inferences that go beyond the explicit information provided in the text. By sticking to the facts and avoiding extrapolation, the model can produce summaries that are both informative and reliable