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Introduction to Mphasis DeepInsights Text Summarizer
- Mphasis Deepinsights Text Summarizer is an advanced deep learning solution that leverages natural language processing and machine learning to learn distributed representation for texts. It uses a deep neural network based model to synthesize large bodies of text into their most important parts. This text summarizer can shorten text summarization models significantly while preserving the document’s most important soundbites.
- By using a deep learning approach, Mphasis Deepinsights Text Summarizer can learn from research and models in language processing and natural language understanding, giving it the ability to understand sentences and documents better than traditional summarization methods.
- It can generate summaries by using extractive text summarization which extracts the most important sentences from a document. It also has a summaries discriminator which can differentiate between real and generated summaries. This allows the user to create summaries that are more accurate and reliable. Additionally, Mphasis Deepinsights Text Summarizer uses GAN-based methods along with pure transformer models to extract words and sentences from an original text.
- It uses Transfer Learning, Transformation models which use self attention, it can have at most 512 words as input and returns the output of about 3 sentences, which is around 30 words.
Applications of Text Summarization in the Enterprise
The application of text summarization in the enterprise is becoming increasingly popular and uses AI to produce automatic text summarization. Text summarization can also be used for other types of media such as videos and audio, helping companies extract important information in the blink of an eye. In the enterprise, text summarization focuses on abstractive and extractive summarization.
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Use Cases in the Enterprise
These are some use cases where Text Summarization can be used across the enterprise:
- Media monitoring
The issue of information overload and “content shock” has been discussed extensively. Automated summaries offer the possibility of reducing a constant stream of information to smaller pieces.
2. Search marketing and SEO
When you are considering SEO for your search queries, it is crucial that you get an all-around view of what competitors are talking about in their content. This has become especially important as Google has updated its algorithm and switched the focus towards authority on topics (rather than keywords). Multi-document summaries can be a powerful tool for rapidly parsing through dozens of search results, understanding common themes, and skimming for the most relevant points.
3. Internal document workflow
Large companies are continuously producing in-house knowledge that is often stored in databases, underutilized, as unstructured data. These companies need to adopt tools that allow them to reuse the knowledge already there. Summarization can allow analysts to quickly learn all that the firm has done so far on any given topic, and to rapidly compile reports incorporating multiple perspectives.
4. Social media marketing
Companies that produce long-form content such as whitepapers, ebooks, and blogs may be able to use summary generation to break down that content and enable sharing it on social media sites such as Twitter or Facebook. This would enable companies to further re-use existing content.
- Question answering and bots
Large-scale summaries can be an effective technique for answering questions. By gathering the most relevant documents to a specific question, the summarizer can compile a coherent response as a summary of multiple documents.
6. Video scripting
Video is becoming one of the most important marketing mediums. Besides video-focused platforms like YouTube, people are now sharing videos on professional networks like LinkedIn. Depending on the type of video, more or less scripting might be required. Summarization can get to be an ally when looking to produce a script that incorporates research from many sources.
7. Medical cases
With the growth of tele-health, there is a growing need to better manage medical cases, which are now fully digital. As telemedicine networks promise a more accessible and open healthcare system, technology has to make the process scalable. Summarization can be a crucial component in the tele-health supply chain when it comes to analyzing medical cases and routing these to the appropriate health professional.
- Email overload
Companies like Slack were born to take our minds off the relentless ping-pong of emails. Summarization can highlight the most important things in an email, allowing us to quickly scan through an email.
- Science and R&D
Academic papers usually include an artificially generated summary, which acts as a table of contents. However, when tasked with keeping track of trends and innovations within a given field, reading each abstract becomes overwhelming. Systems that can cluster papers together and compress the abstracts, even more, could prove helpful to that task.
- Meetings and video-conferencing
With increasing telecommuting, there is an increasing demand for being able to extract insights and critical content from conversations. A system that can convert speech into text and create summaries of team meetings would be great.
11. Help desk and customer support
Knowledge bases have been around for some time now, and are crucial to SaaS platforms for providing support for customers at scale. However, users sometimes can get jumbled up while browsing through the support documentation.
So, we saw how we got some helpful insights out of the longest texts using the AWS Text Summarizer. The intent is to produce a cohesive, fluent summary with just the major points that are outlined in the document. Also, applying Text Summarization decreases reading time, speeds up the process of researching information, and increases the amount of information that can be gathered within a scope.
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1. What are the main approaches to automatic summarization?
ANS: – There are two main types of how to summarize the text in NLP. This article discusses the two main approaches to automatic summarization – extractive and abstractive.
- Extraction-based summarization
- Abstraction-based summarization
2. What is the main purpose of a text Summarizer in Machine Learning?
ANS: – Text summarization is used in a variety of applications, such as interpreting the text, natural language processing, legal text synthesis, news synthesis, and text classification. Machine learning and advanced natural language techniques are used for producing summaries using natural language processing (NLP) tasks.
WRITTEN BY Neetika Gupta
Neetika Gupta works as a Senior Research Associate in CloudThat has the experience to deploy multiple Data Science Projects into multiple cloud frameworks. She has deployed end-to-end AI applications for Business Requirements on Cloud frameworks like AWS, AZURE, and GCP and Deployed Scalable applications using CI/CD Pipelines.
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