Text Summarization is the process of creating a condensed form of text document which maintains significant information and general meaning of source text. Automatic text summarization becomes an important way of finding relevant information precisely in large text in a short time with little efforts. Text summarization approaches are classified into two categories: extractive and abstractive. This paper presents the comprehensive survey of both the approaches in text summarization.

An overview of Text Summarization techniques

Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic text summarization systems are developed. Text summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic text summarization in different languages using various text summarization approaches. The focal centre of this survey paper is to present the research done on text summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of text summarization approaches applied on both Indian and non-Indian languages.

Study of automatic text summarization approaches in different languages

Propelled by the advancements in the field of natural language processing, generating summaries of long texts using various NLP tools and techniques has always been a subject of great interest for scientists all over the world. Data is ubiquitous, and a large amount of data is processed every second in the digital space. For these reasons also, there is need of machine learning algorithms that can automatically shorten longer texts or documents and provide the accurate and meaningful summaries. These summaries find their applications in a wide variety of fields like medicine, market review, business analytics, etc. In other languages like English, an ample amount of study and research can be observed. However, in context of the Indian regional languages, the research in text summarization is very limited and is still in the infancy state. This paper tries to explain the research and works that have been performed in the field of text and document summarization in Indian regional languages.

Review of Text Summarization in Indian Regional Languages

258 DOI: http://dx.doi.org/10.37398/JSR.2020.640149 Abstract: In the era of digital world, online information is growing exponentially. It leads to emergence of inconvenient searching of relevant information in relevant time. In this regard, automatic text summarizer proves to be a good tool. It helps in creating a brief and meaningful form of the given text using natural language tool kit so that users can access the information in quick manner. Today, a lot of summarization tools are available for rich resource languages such as English. But, it seems difficult to summarize the text for Indian languages (low resource languages) due to limited availability of NLP tools and techniques for Indian languages. In this paper, we present a survey on existing text summarization methods and NLP tools for Indian languages. We also discuss about the issues associated with the Indian languages that are the bottlenecks for summarizing Indian language text.

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Accountability of NLP Tools in Text Summarization for Indian Languages

The proposed system investigates the problem of building the domain based single and multiple document Amharic text summarization. Multi-document summarization is the main task in natural language processing and summarizing a huge text document into a short and precise format from multiple documents. Multi-document summarization targets to condense the most important information from a set of documents to produce a short summary. Multi-document summarization is also an integral tool for document understanding and well interpreting in the existing system of text summarization. But single text document summarization has been done from a single text document only. Text summarization can be done based on its input, purpose, and output. In the existing system, most research has been done on extractive single document summarization, but now we propose the new system that solves the existing problem by developing the combinations of extractive and abstractive summarization approach on a single as well as multiple document input from the user. To solve such existing problem by using Java programming language for their flexibility and it has a powerful library Java universal network graphic for text summary. PageRank algorithm plays a great role in finding out their sentence score and its weights of a sentence in the document. The proposed model summarizes only text document but in the future, develop text summarization model for all types of document including graph, image, picture, video and other form in addition to the text document.

Automatic Amharic Text Summarization using NLP Parser

Text Summarization is a method of reducing the original text document into a short description This short version retains the meaning and information content of the original text document It is a difficult task for human beings to generate the summary for very large documents manually The linguistic and statistical features of sentence can be used to find the importance of sentences The Latent Semantic Analysis (LSA) captures automatically the semantic relationships between the sentences as a human being thinks In this paper Singular Value Decomposition (SVD) is used to generate the summary SVD finds the dimensions of the sentence vectors which are principal and mutually orthogonal These properties guaranty the relevance to original text document and non-redundancy respectively in machine generated summary

Geetha J K

Papers

Kannada text summarization using Latent Semantic Analysis