For large inputs (above 1024 tokens) you will need to use the asynchronous mode: see more in the documentation.
What is Summarization?
Text summarization simply is the process of summarizing a block of text in order to make it shorter.
Let's say you have the following block of text:
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.
This technical description is quite long and maybe not all these details are necessary for a common reader to grasp the general idea. So we now want to leverage machine learning in order to automatically summarize this piece of text.
A summarization model would return something like this:
The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world.
Interesting isn't it? As you can see, the general idea is still there, but tons of details were stripped. It makes the text half its initial size!
Why Use Summarization?
Text summarization can be usefully used in many situations. Let's give you a couple of examples.
Some jobs require a huge amount of time dedicated to reading the news. It is especially true in marketing and commercial departments. Feeding analysts with summarized content can help them save a lot of time and energy.
If your company is creating a lot of content on a regular basis, it is very likely that this content has to be summarized after every article creation in order to serve as a headline and be pushed to social networks. Why not automate this?
Legal Documents Parsing
Reading a lot of legal documents everyday is long an exhausting. Sometimes, reading all the details is not vital. In that case, providing people with a summary in addition to the original text can be a great productivity booster.
Writing reports is sometimes compelling for your customers, your management, or your colleagues. Summarization can definitely alleviate this task.
Control whether you want to use the model on a GPU. Machine learning models run much faster on GPUs.
AI models don't always work well with non-English languages.
We do our best to add non-English models when it's possible. See for example Fine-tuned LLaMA 2 70B, Dolphin, ChatDolphin, XLM Roberta Large XNLI, Paraphrase Multilingual Mpnet Base V2, or spaCy. Unfortunately not all the models are good at handling non-English languages.
In order to solve this challenge, we developed a multilingual module that automatically translates your input into English, performs the actual NLP operation, and then translates the result back to your original language. It makes your requests a bit slower but often returns very good results.
Even for models that natively understand non-English languages, they actually sometimes work even better with the multilingual addon.
Simply select your language in the list, and from now on you can write the input text in your own language!
This multilingual add-on is a free feature.
Determines the size of the summary. Possible values are "small" and "large". Defaults to "small".