A Simple Guide To Abstractive Multi-Document Text Summarization And What It Can Do For You

In our modern era, there are vast amounts of data from a variety of sources that are difficult to consolidate. We might miss critical information if we don't pay attention to all the text we are provided.

That's why data science is interested in researching text summarization, and there have been huge advances in Natural Language Processing (NLP) and deep learning over the past few years. Automatic text summarization has enabled us to summarize important content from a document or several documents; however, it is still in its early stages. This guide will help you understand the basic concepts of abstractive multi-document text summarization and how we can use it.

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Abstractive vs Extractive Summarization

When someone is given a text to summarize, they rewrite the main points of the text in their own words. That is called abstractive summarization, and it needs high-level summarization skills that are present in humans. There are advancements in this field, but abstractive summarization systems are still not up to par, and that's why systems opt for extractive summarization. In this technique, sentences are taken directly from the input documents and presented in a readable way. The main idea of the input documents is not rephrased, however, the AI-empowered techniques identify the important content and extract it from the documents.

Single vs Multi-Document Summarization

The system extracts the important information from a document unhindered by repetition when summarizing a single document. On the other hand, multi-document summarization systems are faced with redundancy as they scan several documents. They should produce a summary that includes the important information in all the input documents while reducing repetition.

Examples of Text Summarization

There are many reasons and uses for automatic text summarization. Instead of going through large documents to find the main points of the documents or to find some information you need, an automatic text summarization system will provide them to you easily. Some of the examples of text summarization are:

News headlines
Outlines for curriculums
Minutes of meeting
Biography
Stock market reports
Weather forecasts
History

What Can Abstractive Multi-Document Text Summarization Do For You?

Abstractive multi-document text summarization (AMDTS) systems are the most sophisticated automatic text summarization system as they extract information from various documents and rephrase the main points without repetition. The possibilities that AMDTS systems are offering are phenomenal though it is still a new concept. Here are some uses of AMDTS that can help you.

Companies Internal Document Workflow

Large businesses are continually producing vast amounts of internal knowledge that are underutilized because the task of summarizing these reports and documents will take lots of working hours. AMDTS will enable analysts in these companies to have a useful output of all the amassed knowledge, understand what the company has done regarding a specific topic, and assemble reports of corporate different points of view.

SEO

In order to understand what the latest SEO trends are and evaluate if your practices are working or not, you need to know how the competitors are working and what content they share. AMDTS can help you summarize dozens of search results, analyze the most important points, and figure out shared themes.

Personal Assistant Bots

Personal assistant technology is available right now to everyone on their devices. You can ask your phone where the nearest restaurant is, and it will answer you. However, personal assistants are still limited as they can't answer more complicated questions. AMDTS will empower this question answering technique by collecting the most relevant documents regarding a question, then form an answer out of the summarization of these documents.

Medical Cases

Technology has helped medicine considerably in the last couple of decades. However, it became essential to manage medical cases more efficiently. With the right application of AMDTS, a system can through past cases and evaluate the right course of action, or receive the symptoms of a patient and cross-reference it with similar cases and reroute the patient to the right doctor.

Meetings

As remote work grew significantly over the last few years, the need to summarize meetings and video conferences have grown. Imagine a system that can turn voice to text then summarize the main ideas of the meeting into one single document.

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The future of AMDTS is promising as its real-world applications can revolutionize the way we do business. This technique is still in its early stages, and it doesn't fully produce the results we hope for. However, several technological advancements in data science will enable AMDTS to function more effectively in the near future.