Machine Learning for Automatic Text Summarization

Summarization is the process of compressing a piece of text into a shorter version, lowering the size of the original text while keeping vital informative aspects and content meaning. Because manual text summarizing is a time-consuming and typically arduous activity, automating the work is gaining popularity and thus serves as a major impetus for academic study.

Text summarizing has vital applications in a variety of NLP tasks such as text categorization, question answering, legal text summarization, news summary, and headline generation. Furthermore, the development of summaries may be implemented into these systems as an intermediary stage, which helps to shorten the document's length.

The volume of text data from various sources has increased dramatically in the big data age. This volume of material has a wealth of information and expertise that must be adequately summarized in order to be useful. The growing availability of documents has necessitated much study in the NLP field for automated text summarization. The job of creating a succinct and fluent summary without the assistance of a person while keeping the sense of the original text material is known as automatic text summarization.

It is really difficult since, when we humans summarize a piece of material, we normally read it completely to expand our knowledge before writing a summary highlighting its important elements. Because computers lack human understanding and linguistic competence, automated text summarization is a complex and time-consuming operation.

For this goal, several machine learning methods have been proposed. The majority of these techniques represent this challenge as a classification problem that outputs whether or not a sentence should be included in the summary. Topic information, Latent Semantic Analysis (LSA), Sequence to Sequence models, Reinforcement Learning, and Adversarial processes have all been applied in other techniques.

There are two ways to automated summarization in general: extraction and abstraction.

The extractive approach

Extractive summarization uses a scoring mechanism to extract sentences from a document and combine them to generate a logical summary. This approach works by detecting essential areas of the text and clipping and stitching together bits of the information to create a shortened version.

As a result, they rely only on phrase extraction from the original text. The majority of summarizing research nowadays has concentrated on extractive summarization since it is simpler and produces naturally grammatical summaries that need minimal language study. Furthermore, extractive summaries include the most essential lines from the input, which might be a single document or a collection of papers.

Abstractive summarization

Abstractive summarization methods aim to produce summaries by interpreting the text using advanced natural language techniques in order to generate a new shorter text — parts of which may not appear in the original document — that conveys the most critical information from the original text, requiring rephrasing sentences and incorporating information from full text to generate summaries, as a human-written abstract usually does. An adequate abstractive summary, in fact, covers key information in the input and is linguistically fluent.

As a result, they are not limited to just choosing and rearranging sections from the original text.

Abstractive approaches make use of current advances in deep learning. Abstractive approaches capitalize on the recent success of sequence to sequence models since it may be viewed as a sequence mapping problem in which the source text should be transferred to the target summary. These models are made up of an encoder and a decoder, with a neural network reading the text, encoding it, and then generating target text.

In general, creating abstract summaries is a difficult process that is more difficult than data-driven alternatives such as sentence extraction and requires advanced language modeling. As a result, despite recent improvements employing neural networks inspired by the success of neural machine translation and sequence to sequence models, they are still far from approaching human-level quality in summary creation.