differences-between-extractive-and-abstractive-summary

Differences between extractive and abstractive summary

We are in Big Data era and, as years passed, we have understood that data is not only about tables and numbers. Data professionals talk more and more about unstructured data, such as texts, images and video. Texts seem to be a high-potential source: if you are able to automatically analyze text, you can extract important insights from your document, email and web pages! The volume of this text data is by far too much for a human being and that is why is more and more important to be able to summarize it with automatic tools. 

If you try to search on Google, you will realize that there are many automatic tools, free or freemium, to summarize a pdf online or, more in general, a set of words. Which are the characteristics to choose a tool instead of another? It is obvious: the ability to extract important part of the text and to re-shape them in a natural language phrase. The summarization process for a machine is very complicated, it’s not only a matter of grammar or semantics, it is a matter of understanding the real message of the text and to come out with a new phrase. 

Generally, automatic text summarization tool to summarize pdf online are based on sophisticated algorithms, developed by experts of Natural Language Processing. The ability of machines to learn from data is essential to analyze text but, coming back to the basis, which are the main techniques of text summarization? In this article, we try to define them in a simple way.

Two approaches to text summarization

Basically, there are two main approaches to summarize pdf online: extraction and abstraction. These approaches have been deeply explained by the related literature.

The first one, extraction, involves concatenating extracts taken from the corpus into a summary. The second, abstraction, involves generating novel sentences from information extracted from the corpus. In other words, extractive summarization aims at identifying the salient information, extract them and group together to form a concise summary. Abstractive summary generation rewrites the entire document by building internal semantic representation, and then a summary is created using natural language processing.  

So far, to be honest, summary evaluation is a challenging open research area, which is becoming more and more important as more automatic text summarization tools are developed. A possibility is to compare human-written model summary and machine summary. Some authors used this method and the results suggest that, while the abstractive summarizer performs better overall, the margin by which abstraction outperforms extraction is greater when controversiality is high.

To put it in simple terms, the extractive approach could be inappropriate in the context of multi-document summarization of news or articles. In this context, the use of extractive system can produce summaries overly verbose or biased towards some sources. Moreover, in the extraction approach, it could be that there is a high frequency of certain words or they are not put in the right order. In reverse, the abstractive summarization is more complex, but it can be extremely more effective in difficult situation.

Extractive approach: which implementation techniques?

Extractive summarization techniques involve: the construction of an intermediate representation of the input text (text to be summarized); he creation of a scoring of the sentences; the selection of the top K most important sentences. The extractive approach contains different implementation techniques: the important point is to understand how to intermediate representation of the input text is realized. Generally, there are two main categories: topic representation or indicator representation. The first category is based on the analysis of the topics and it ranks each sentence based on the number of topics the sentence contains. The second one chooses an indicator, which can be sentence length or sentence position, to rank the sentences.

Extractive and Abstractive methods in automatic text summarization tool

From students to professionals, from digital publishers to content writers, the possibility to summarize pdf online could be very important to be more efficient in your work. Thanks to Artificial Intelligence algorithms, many tools are being born that helps you to automatic summarize a text. Those programmers use a variety of techniques to help machines understand natural language. Text summarization is a formidable challenge in the field of Natural Language Processing (NLP), indeed.

The main techniques, as we have seen, are two: extraction and abstraction. A good automatic text summarization tool, nowadays, in most of the cases use only the extractive approach, while the abstractive one is the most interesting for the future. The abstractive method involves, as anticipated, the real ability for a machine to understand the semantics of the text and to create new phrases using natural language.

PaperLit, tech company of Datrix Group, has created an automatic text summarization tool that applies part of the approaches described so far. The tool is used both in many solutions provided by PaperLit and as a free web tool for summarize pdf online.

 

Comments are closed.