#textsummarization

Which metric is commonly used to evaluate the quality of a text summarization system?

Text summarization is an important Natural Language Processing (NLP) task that is bound to have a considerable impact on our lives. With the evolution of the digital world and its consequent impact on the growing publishing industry, dedicating time to sincerely read an article, document, or book to decide its relevance is no longer a feasible option, especially considering time scarcity.

Further, with the increasing number of articles being published accompanied by the digitization of traditional print publications, it has become nearly impossible to keep track of incrementing publications available on the web. This is where text summarization can help to reduce the texts with less time intricacy.

What is Text Summarization?

The technique, where a computer program shortens longer texts and generates summaries to pass the intended message, is defined as Automatic Text Summarization and is a common problem in machine learning and natural language processing (NLP).

Text summarization is the process of creating a short, coherent, and fluent summary of a longer text document and involves the outlining of the text’s major points.

Text identification, interpretation and summary generation, and analysis of the generated summary are some of the key challenges faced in the process of text summarization.

The critical tasks in extraction-based summarization are identifying key phrases in the document and using them to discover relevant information to be included in the summary.

Two different approaches that are used for text summarization are:

 Extractive Summarization

 Abstractive Summarization

Extractive Summarization

Extractive methods attempt to summarize articles by identifying the important sentences or phrases from the original text and stitch together portions of the content to produce a condensed version. These extracted sentences are then used to form the summary.

Abstractive Summarization

This technique, unlike extraction, relies on being able to paraphrase and shorten parts of a document using advanced natural language techniques. Since abstractive machine learning algorithms can generate new phrases and sentences to capture the meaning of the source document. When such abstraction is done correctly in deep learning problems, they can assist in overcoming grammatical inaccuracies.

Benefits

The benefits of Automatic Text Summarization go beyond solving apparent problems.

Some other advantages of Text Summarization include:

Saves Time: By generating automatic summaries, text summarization helps content editors save time and effort, which otherwise is invested in creating summaries of articles manually.

Instant Response: It reduces the user’s effort involved in exacting the relevant information. With automatic text summarization, the user can summarise an article in just a few seconds by using the software, thereby decreasing their reading time.

Increases Productivity Level: Test Summarization enables the user to scan through the contents of a text for accurate, brief, and precise information. Therefore, the tool saves the user from the workload by reducing the size of the text and increasing the productivity level as the user can channel their energy to other critical things.

Ensures All Important Facts are Covered:

The human eye can miss crucial details; however, automatic software does not.  What every reader requires is to be able to pick out what is beneficial to them from any piece of content. The automatic text summarization technique helps the user gather all the essential facts in a document with ease. Text Summarization Powered by Impelsys’ SSAM.ai Automatic text summarization, an exclusive feature of SSAM.ai generates a semantically coherent and meaningful summary of the learning content while retaining the key information and overall meaning. It automatically summarizes sizable chunks of learning content into concise and meaningful summaries.

Impelsys’ Text Summarization Approach Impelsys Text Summarization:

 Enables automated summarization of lengthy, text-rich content.

 Facilitates flashcard creation.

 Reduces the time needed for E-learning by comprehending the source.

Weighting Feature Extraction for Text Summarization of Documents in Indonesian Language Using Genetic Algorithm

Important things from automatic text summarization are how to determine important information from a document. Important information can be obtained using extraction technique. Extraction technique is a technique which consists of the complete sequences are copied sentences and choose the parts important sentences from the original documents. 

Automatic text summarization with extraction techniques can be done by using some of the features text extraction. Each feature has a text extraction rate of different influences on the results of the system summary. Therefore, needed an optimization algorithm for determine level of importance or weight value of each feature extraction. One optimization algorithm that can be used is a genetic algorithm. On this final project, genetic algorithms are used to optimize the weight of feature extraction. 

In the training phase, the genetic algorithm is able to optimize the weight of the text extraction feature that produces an accuracy of about 46%. While in the testing phase, the system generates a summary with an accuracy of 46% for ten text features. After the observation model of the best chromosome, the system generates a summary with 53% accuracy for eight feature text extraction.