Summarization

For large inputs (above 128,000 tokens) you will need to use the asynchronous mode: see more in the documentation.

API key (get one here)

Language (multilingual addon)

Model

Use GPU

Summary Size

Text to summarize

As the wind is intermittent, when the demand is not wind dependant (nobody intends to have a fridge that doesn’t operate when there is no wind!), an electricity produced by wind only should be produced in excess then stored when there is wind, to be restituted when the consumer intends to be served. If the storage is done with “chemical” devices, we can use a battery or convert electricity into hydrogen, and if we use the means of mechanical energy, we can for example use water stored in altitude, in a dam lake.

If all the electricity production in France was wind generated, storage in batteries would probably be impossible to ensure: to store one week of electricity consumption (which is over1 TWh per day for the whole country), the country should have 7 tons of lead-acid batteries (these batteries can store 30 Wh per kg) per French person (lead-acid batteries are not precisely perfectly “clean” regarding the environmental impact of their manufacturing and their end of life…).

A probably more realistic solution would consist in producing hydrogen when there is wind (through electrolysis), then use it in fuel cells when there is no wind. The efficiency of electrolysis is 80% at best, that of storage is also 80% at best (there must be an energy spending to compress the hydrogen into a reservoir!), and then the best fuel cells have an efficiency of 80% in cogeneration (which means promoting electric heating, in a way!) but of 45% fort the sole electricity production.

In this last case, the efficiency of the whole storage chain is 28%. If we suppose that half of the electricity is used when it is produced, but that the other half has to be stored, with an efficiency of 25%, then we need roughly 600,000 windmills of 1 MW to produce 500 TWh (that is 125.000 machines that produce without storage, and 500.000 that produce with storage, hence, with an efficiency divided by 4, the total representing the equivalent of 250,000 machines without storage).

If we store through elevating water, here is a hint on the scale of what is required: in France, dams produce today 7% of the total electricity consumed (and represent 20% of the installed production power). It means that having enough water stored to supply the whole France when there is no wind requires to multiply dam lakes by 5 to 6 at least (so that dams alone can provide France with 100% of its peak consumption), then have the same surface of lakes downstream to recuperate the water after it is turbined to put it back in the upstram reservoir. We should also take into account the energy losses linked to the friction when elevating water back in upstream reservoirs (with the electricity delivered by wind turbines on windy days, which is roughly 25%).

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.

News Review

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.

Content Creation

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.

Reports Generation

Writing reports is sometimes compelling for your customers, your management, or your colleagues. Summarization can definitely alleviate this task.