Semantic Analysis Based Text Summarization

Automatic summarization has become an important part in the study of natural language processing since the advent of the 21st century, since a majority of the data online is textual. Summarization of text will lead to a reduction of data while maintaining the context of it. Having such summarization activity being done automatically also helps in reducing human effort. Summarization is the process of generation of the summary of input text by extracting the representative sentences from it. In this project, we present a novel technique for generating the summarization of domain specific text by using Semantic Analysis for text summarization, which is a subset of Natural Language Processing

Text Summarization: Taking Legal Document Summarization as an Example

Legal Document Summarization is an automated text summarization system which is generated by a computer program. This projects aim to generate a relevant summary from a legal tender specifically documents on tender. With the development of this module, it is hoped that this will decrease the time required for handling tender process, eliminating the need on using manual summarizing and providing an easy viewing for user. This program will be developed by incorporating Artificial Intelligence field of Natural Language Processing (NLP) techniques and also finding the most suitable methodology to handle a project development that deals on text summarization processes. Therefore a custom-made methodology are implemented which are based on SDLC methodology and a summarization process. In incorporating NLP technique, based on the existing summarization system technique on word counting and clue phrases for topic identification and word clustering are used for better interpretation of information. Apart from using NLP, other techniques such as theme extraction are also taken into consideration for better generation of the summary based on the relevant requirement for the document. With this, extraction of texts based on the results from word counting and theme extraction can be generated. The technology that are being generated here are for a single document summarization in English language

Analysis of Abstractive and Extractive Summarization Methods

This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted

Text Summarization

Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks) [2]. By providing a text summarization system that will simplify the bulk of information and producing only the most important points, the task of reading and understanding a text would inevitably be made easier and faster. With a large volume of text documents, a summary of each document greatly facilitates the task of finding the desired documents and the desired data from the documents. As a solution for the above matter, this project objective is to simplify the texts from a previous text summarization system and further reducing the number of words in a sentence, shortening the sentences and eliminating sentences with similar meanings and also produce grammar rules that generate sentences that are human-like. The waterfall model is chosen as the project development life cycle. A detailed research has been conducted during the requirement definition phase and the system prototype is designed in the system and software design phase. During the development phase, the coding implementation will be conducted and the unit testing part will be done throughout that development process. After the entire unit has been tested, they will be integrated together and the system testing can be done as a whole. The complete program is put through thorough test and evaluation to ensure its functionality and efficiency. As the conclusion, this project should be able to produce a summarized text as the output product and meet the project requirements and objectives