Auto Text Summarization
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 11
Abstract
Summaries are an important tool for familiarizing oneself with a subject area. Text summaries are essential when forming an opinion on if reading a document in whole is necessary for our further knowledge acquiring or not. In other words, summaries save time in our daily work. To write a summary of a text is a non-trivial process where one, on one hand has to extract the most central information from the original text, and on the other has to consider the reader of the text and her previous knowledge and possible special interests. Today there are numerous documents, papers, reports and articles available in digital form, but most of them lack summaries. The information in them is often too abundant for it to be possible to manually search, sift and choose which knowledge one should acquire. This information must instead be automatically filtered and extracted in order to avoid drowning in it. Automatic Text Summarization is a technique where a computer summarizes a text. A text is given to the computer and the computer returns a shorter less redundant extract of the original text. So far automatic text summarization has not yet reached the quality possible with manual summarization, where a human interprets the text and writes a completely new shorter text with new lexical and syntactic choices. However, automatic text summarization is untiring, consistent and always available. Generally speaking there are at least two properties of the summary that must be measured when evaluating summaries and summarization systems - the Compression Ratio, i.e. how much shorter the summary is than the original, and the Retention Ratio, i.e. how much of the central information is retained. This can for example be accomplished by comparison with existing summaries for the given text. Digitally stored information is available in abundance and in a myriad of forms to an extent as to making it near impossible to manually search, sift and choose which information one should incorporate. This information must instead be filtered and extracted in order to avoiding drowning in it.
Authors and Affiliations
Tarun Dugar, Rijul Handa, Mohit Garg, Akshay Gupta, Rachna Jain
Keywords
Related Articles
- EP ID EP19067
- DOI -
- Views 307
- Downloads 12
A Hybrid System of 7 Level Inverter Based Novel ZVT-ZCT-PWM Boost Converter System
We propose here a new boost converter with an active snubber cell is proposed. The active snubber cell provides main switch to turn ON with zero-voltage transition (ZVT) and to turn OFF with zero-current transition (ZCT...
Implementation of an Efficient Way of Broadcasting Response Message to Help Demanding Vehicle in VANET
VANET is very demanding area of research in these days. In this paper, we identify that when an emergency condition occurred at any location on the road, then emergency support take time to reach emergency location due...
Resisting Malicious and Packet Dropping Attacks in the Presence of Collisions in Wireless ADHOC Networks
In wireless ad hoc networks, a primary requirement for the establishment of communication among nodes is that nodes should cooperate with each other. Link error and malicious packet dropping are two sources for packet l...
Assessment of Groundwater potential zone using remote sensing and GIS in Varahanadhi watershed, Tamilnadu, India
Groundwater is an important natural resource for its use in domestic, agriculture, and industries purposes. There has been a tremendous increase in the demand for groundwater due to increase in population, advanced irri...
slugTrapdoor Exposure using Markov Model
Mobile network is one of most common ad hoc network with problems related to congestion and routing. Trapdoor exposure disrupts routing protocols by short circuiting the normal flow of routing packets. Such a type of at...