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A Quick Introduction to Text Summarization in Machine Learning

2018-09-19 - By 

Text summarization refers to the technique of shortening long pieces of text. The intention is to create a coherent and fluent summary having only the main points outlined in the document.

Automatic text summarization is a common problem in machine learning and natural language processing (NLP).

Skyhoshi, who is a U.S.-based machine learning expert with 13 years of experience and currently teaches people his skills, says that “the technique has proved to be critical in quickly and accurately summarizing voluminous texts, something which could be expensive and time consuming if done without machines.”

Machine learning models are usually trained to understand documents and distill the useful information before outputting the required summarized texts.

What’s the need for text summarization?

Propelled by the modern technological innovations, data is to this century what oil was to the previous one. Today, our world is parachuted by the gathering and dissemination of huge amounts of data.

In fact, the International Data Corporation (IDC) projects that the total amount of digital data circulating annually around the world would sprout from 4.4 zettabytes in 2013 to hit 180 zettabytes in 2025. That’s a lot of data!

With such a big amount of data circulating in the digital space, there is need to develop machine learning algorithms that can automatically shorten longer texts and deliver accurate summaries that can fluently pass the intended messages.

Furthermore, applying text summarization reduces reading time, accelerates the process of researching for information, and increases the amount of information that can fit in an area.

What are the main approaches to automatic summarization?

There are two main types of how to summarize text in NLP:

  • Extraction-based summarization

The extractive text summarization technique involves pulling keyphrases from the source document and combining them to make a summary. The extraction is made according to the defined metric without making any changes to the texts.

Here is an example:

Source text: Joseph and Mary rode on a donkey to attend the annual event inJerusalem. In the city, Mary gave birth to a child named Jesus.

Extractive summary: Joseph and Mary attend event Jerusalem. Mary birth Jesus.

As you can see above, the words in bold have been extracted and joined to create a summary — although sometimes the summary can be grammatically strange.

  • Abstraction-based summarization

The abstraction technique entails paraphrasing and shortening parts of the source document. When abstraction is applied for text summarization in deep learning problems, it can overcome the grammar inconsistencies of the extractive method.

The abstractive text summarization algorithms create new phrases and sentences that relay the most useful information from the original text — just like humans do.

Therefore, abstraction performs better than extraction. However, the text summarization algorithms required to do abstraction are more difficult to develop; that’s why the use of extraction is still popular.

Here is an example:

Abstractive summary: Joseph and Mary came to Jerusalem where Jesus was born.

How does a text summarization algorithm work?

Usually, text summarization in NLP is treated as a supervised machine learning problem (where future outcomes are predicted based on provided data).

Typically, here is how using the extraction-based approach to summarize texts can work:

1. Introduce a method to extract the merited keyphrases from the source document. For example, you can use part-of-speech tagging, words sequences, or other linguistic patterns to identify the keyphrases.

2. Gather text documents with positively-labeled keyphrases. The keyphrases should be compatible to the stipulated extraction technique. To increase accuracy, you can also create negatively-labeled keyphrases.

3. Train a binary machine learning classifier to make the text summarization. Some of the features you can use include:

  • Length of the keyphrase
  • Frequency of the keyphrase
  • The most recurring word in the keyphrase
  • Number of characters in the keyphrase

4. Finally, in the test phrase, create all the keyphrase words and sentences and carry out classification for them.

Summary

Text summarization is an interesting machine learning field that is increasingly gaining traction. As research in this area continues, we can expect to see breakthroughs that will assist in fluently and accurately shortening long text documents.

Original article here.

 


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MIT is aiming for AI moonshots with Intelligence Quest

2018-02-01 - By 

Artificial intelligence has long been a focus for MIT. The school’s been researching the space since the late ’50s, giving rise (and lending its name) to the lab that would ultimately become known as CSAIL. But the Cambridge university thinks it can do more to elevate the rapidly expanding field.

This week, the school announced the launch of the MIT Intelligence Quest, an initiative aimed at leveraging its AI research into something it believes could be game-changing for the category. The school has divided its plan into two distinct categories: “The Core” and “The Bridge.”

“The Core is basically reverse-engineering human intelligence,” dean of the MIT School of Engineering Anantha Chandrakasan tells TechCrunch, “which will give us new insights into developing tools and algorithms, which we can apply to different disciplines. And at the same time, these new computer science techniques can help us with the understanding of the human brain. It’s very tightly linked between cognitive science, near science and computer science.”

The Bridge, meanwhile, is designed to provide access to AI and ML tools across its various disciplines. That includes research from both MIT and other schools, made available to students and staff.

“Many of the products are moonshoots,” explains James DiCarlo, head of the Department of Brain and Cognitive Sciences. “They involve teams of scientists and engineers working together. It’s essentially a new model and we need folks and resources behind that.”

Funding for the initiative will be provided by a combination of philanthropic donations and partnerships with corporations. But while the school has had blanket partnerships in the past, including, notably, the MIT-IBM Watson AI Lab, the goal here is not to become beholden to any single company. Ideally the school will be able to work alongside a broad range of companies to achieve its large-scale goals.

“Imagine if we can build machine intelligence that grows the way a human does,” adds professor of Cognitive Science and Computation, Josh Tenenbaum. “That starts like a baby and learns like a child. That’s the oldest idea in AI and it’s probably the best idea… But this is a thing we can only take on seriously now and only by combining the science and engineering of intelligence.”

Original article here.

 


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Tech trends for 2017: more AI, machine intelligence, connected devices and collaboration

2016-12-30 - By 

The end of year or beginning of year is always a time when we see many predictions and forecasts for the year ahead. We often publish a selection of these to show how tech-based innovation and economic development will be impacted by the major trends.

A number of trends reports and articles have bene published – ranging from investment houses, to research firms, and even innovation agencies. In this article we present headlines and highlights of some of these trends – from Gartner, GP Bullhound, Nesta and Ovum.

Artificial intelligence will have the greatest impact

GP Bullhound released its 52-page research report, Technology Predictions 2017, which says artificial intelligence (AI) is poised to have the greatest impact on the global technology sector. It will experience widespread consumer adoption, particularly as virtual personal assistants such as Apple Siri and Amazon Alexa grow in popularity as well as automation of repetitive data-driven tasks within enterprises.

Online streaming and e-sports are also significant market opportunities in 2017 and there will be a marked growth in the development of content for VR/AR platforms. Meanwhile, automated vehicles and fintech will pose longer-term growth prospects for investors.

The report also examines the growth of Europe’s unicorn companies. It highlights the potential for several firms to reach a $10 billion valuation and become ‘decacorns’, including BlaBlaCar, Farfetch, and HelloFresh.

Alec Dafferner, partner, GP Bullhound, commented, “The technology sector has faced up to significant challenges in 2016, from political instability through to greater scrutiny of unicorns. This resilience and the continued growth of the industry demonstrate that there remain vast opportunities for investors and entrepreneurs.”

Big data and machine learning will be disruptors

Advisory firm Ovum says big data continues to be the fastest-growing segment of the information management software market. It estimates the big data market will grow from $1.7bn in 2016 to $9.4bn by 2020, comprising 10 percent of the overall market for information management tooling. Its 2017 Trends to Watch: Big Data report highlights that while the breakout use case for big data in 2017 will be streaming, machine learning will be the factor that disrupts the landscape the most.

Key 2017 trends:

  • Machine learning will be the biggest disruptor for big data analytics in 2017.
  • Making data science a team sport will become a top priority.
  • IoT use cases will push real-time streaming analytics to the front burner.
  • The cloud will sharpen Hadoop-Spark ‘co-opetition’.
  • Security and data preparation will drive data lake governance.

Intelligence, digital and mesh

In October, Gartner issued its top 10 strategic technology trends for 2017, and recently outlined the key themes – intelligent, digital, and mesh – in a webinar.  It said that autonomous cars and drone transport will have growing importance in the year ahead, alongside VR and AR.

“It’s not about just the IoT, wearables, mobile devices, or PCs. It’s about all of that together,” said Cearley, according to hiddenwires magazine. “We need to put the person at the canter. Ask yourself what devices and service capabilities do they have available to them,” said David Cearley, vice president and Gartner fellow, on how ‘intelligence everywhere’ will put the consumer in charge.

“We need to then look at how you can deliver capabilities across multiple devices to deliver value. We want systems that shift from people adapting to technology to having technology and applications adapt to people.  Instead of using forms or screens, I tell the chatbot what I want to do. It’s up to the intelligence built into that system to figure out how to execute that.”

Gartner’s view is that the following will be the key trends for 2017:

  • Artificial intelligence (AI) and machine learning: systems that learn, predict, adapt and potentially operate autonomously.
  • Intelligent apps: using AI, there will be three areas of focus — advanced analytics, AI-powered and increasingly autonomous business processes and AI-powered immersive, conversational and continuous interfaces.
  • Intelligent things, as they evolve, will shift from stand-alone IoT devices to a collaborative model in which intelligent things communicate with one another and act in concert to accomplish tasks.
  • Virtual and augmented reality: VR can be used for training scenarios and remote experiences. AR will enable businesses to overlay graphics onto real-world objects, such as hidden wires on the image of a wall.
  • Digital twins of physical assets combined with digital representations of facilities and environments as well as people, businesses and processes will enable an increasingly detailed digital representation of the real world for simulation, analysis and control.
  • Blockchain and distributed-ledger concepts are gaining traction because they hold the promise of transforming industry operating models in industries such as music distribution, identify verification and title registry.
  • Conversational systems will shift from a model where people adapt to computers to one where the computer ‘hears’ and adapts to a person’s desired outcome.
  • Mesh and app service architecture is a multichannel solution architecture that leverages cloud and serverless computing, containers and microservices as well as APIs (application programming interfaces) and events to deliver modular, flexible and dynamic solutions.
  • Digital technology platforms: every organization will have some mix of five digital technology platforms: Information systems, customer experience, analytics and intelligence, the internet of things and business ecosystems.
  • Adaptive security architecture: multilayered security and use of user and entity behavior analytics will become a requirement for virtually every enterprise.

The real-world vision of these tech trends

UK innovation agency Nesta also offers a vision for the year ahead, a mix of the plausible and the more aspirational, based on real-world examples of areas that will be impacted by these tech trends:

  • Computer says no: the backlash: the next big technological controversy will be about algorithms and machine learning, which increasingly make decisions that affect our daily lives; in the coming year, the backlash against algorithmic decisions will begin in earnest, with technologists being forced to confront the effects of aspects like fake news, or other events caused directly or indirectly by the results of these algorithms.
  • The Splinternet: 2016’s seismic political events and the growth of domestic and geopolitical tensions, means governments will become wary of the internet’s influence, and countries around the world could pull the plug on the open, global internet.
  • A new artistic approach to virtual reality: as artists blur the boundaries between real and virtual, the way we create and consume art will be transformed.
  • Blockchain powers a personal data revolution: there is growing unease at the way many companies like Amazon, Facebook and Google require or encourage users to give up significant control of their personal information; 2017 will be the year when the blockchain-based hardware, software and business models that offer a viable alternative reach maturity, ensuring that it is not just companies but individuals who can get real value from their personal data.
  • Next generation social movements for health: we’ll see more people uniting to fight for better health and care, enabled by digital technology, and potentially leading to stronger engagement with the system; technology will also help new social movements to easily share skills, advice and ideas, building on models like Crohnology where people with Crohn’s disease can connect around the world to develop evidence bases and take charge of their own health.
  • Vegetarian food gets bloodthirsty: the past few years have seen growing demand for plant-based food to mimic meat; the rising cost of meat production (expected to hit $5.2 billion by 2020) will drive kitchens and laboratories around the world to create a new wave of ‘plant butchers, who develop vegan-friendly meat substitutes that would fool even the most hardened carnivore.
  • Lifelong learners: adult education will move from the bottom to the top of the policy agenda, driven by the winds of automation eliminating many jobs from manufacturing to services and the professions; adult skills will be the keyword.
  • Classroom conundrums, tackled together: there will be a future-focused rethink of mainstream education, with collaborative problem solving skills leading the charge, in order to develop skills beyond just coding – such as creativity, dexterity and social intelligence, and the ability to solve non-routine problems.
  • The rise of the armchair volunteer: volunteering from home will become just like working from home, and we’ll even start ‘donating’ some of our everyday data to citizen science to improve society as well; an example of this trend was when British Red Cross volunteers created maps of the Ebola crisis in remote locations from home.

In summary

It’s clear that there is an expectation that the use of artificial intelligence and machine learning platforms will proliferate in 2017 across multiple business, social and government spheres. This will be supported with advanced tools and capabilities like virtual reality and augmented reality. Together, there will be more networks of connected devices, hardware, and data sets to enable collaborative efforts in areas ranging from health to education and charity. The Nesta report also suggests that there could be a reality check, with a possible backlash against the open internet and the widespread use of personal data.

Original article here.


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