An Effective Example of a Text Summariser Algorithm (of Base-A) in Practice
Prof. Jorgen Lordish | 19.05.2013 23:17 | Analysis | Cambridge | Oxford
This mini study demonstrates how using a simple Base-A text Summarising Algorithm one can get the most important content out of articles without wasting time by reading meaningless text out of articles. By comparing news article's simple text, we demonstrate, how effective a Text Summariser Algorithm can really be, and how much time it save researcher's time. Key Information Content, Compact Text Writing and Informative Text Production are today's demands on Scientific Articles and writing articles to several other vital areas, such as news articles.
An Effective Example of a Text Summariser Algorithm (of Base-A) in Practice
This mini study demonstrates how using a simple Base-A text Summarising Algorithm one can get the most important content out of articles without wasting time by reading meaningless text out of articles. By comparing news article's simple text, we demonstrate, how effective a Text Summariser Algorithm can really be, and how much time it save researcher's time. Key Information Content, Compact Text Writing and Informative Text Production are today's demands on Scientific Articles and writing articles to several other vital areas, such as news articles.
The original article goes through a semantic analysis, from which are constructed some representation of the meaning of the text. This text is then used as an Input to an text generator producing the summarised text. Generator preserves the key issues of the text and squeezes the text according to the given restrictions.
We are applying these bases to the original news article we are about to Summarise. The Article can be found below (N.b. this article has not bee summarised yet).
“Neighbour's Hairy Boyfriend
Finland – Most of us are very like to get new neighbours every now and then. This happened also to me a forth-night ago. Unfortunately, I never have had time to meet my new neighbour before few days ago. But, based on what I've overheard, is her boyfriend very hairy, and based on the traffic of the corridor, he must also be very popular among The International and Domestic Students. He can also speak – Vuf. Should I be worried?”
By applying Base-A Text Summarising Algorithm into the text and applying 50% summarising level we are able to produce the following summarization of the original text:
“Neighbour's Hairy Boyfriend
Most of us get new neighbours now and then. Also me a forth-night ago. I never have had time to meet my neighbour. I've overheard her boyfriend very hairy and popular among students. He can speak. I worried.”
By setting the level of summarizing into maximum, 100%, the algorithm brings the following result compared to the original text:
”Neighbour's Hairy Boyfriend
Vuf.”
As we have now clearly demonstrated, can Text Summariser assembled with a proper algorithm result the most important content of the text very briefly, which can be an advantage to scientist, so they do not have to read so much.
Of applying the UK-HM algorithm the result could be even further summarised; By replacing The Base-A algorithm with UK-HM, string is can be summarised into the most shortest form, what we scientist call, the Royal form: “Vuf”. However, due to some non commercial restrictions we not able to use it and we will have accept a bit longer form for the public access.
Prof. Jorgen Lordish
Department of Politics and Terrorism
Malmö Tekniska Universität
Category: Comedy news, partly true, mostly nonsense.
This mini study demonstrates how using a simple Base-A text Summarising Algorithm one can get the most important content out of articles without wasting time by reading meaningless text out of articles. By comparing news article's simple text, we demonstrate, how effective a Text Summariser Algorithm can really be, and how much time it save researcher's time. Key Information Content, Compact Text Writing and Informative Text Production are today's demands on Scientific Articles and writing articles to several other vital areas, such as news articles.
The original article goes through a semantic analysis, from which are constructed some representation of the meaning of the text. This text is then used as an Input to an text generator producing the summarised text. Generator preserves the key issues of the text and squeezes the text according to the given restrictions.
We are applying these bases to the original news article we are about to Summarise. The Article can be found below (N.b. this article has not bee summarised yet).
“Neighbour's Hairy Boyfriend
Finland – Most of us are very like to get new neighbours every now and then. This happened also to me a forth-night ago. Unfortunately, I never have had time to meet my new neighbour before few days ago. But, based on what I've overheard, is her boyfriend very hairy, and based on the traffic of the corridor, he must also be very popular among The International and Domestic Students. He can also speak – Vuf. Should I be worried?”
By applying Base-A Text Summarising Algorithm into the text and applying 50% summarising level we are able to produce the following summarization of the original text:
“Neighbour's Hairy Boyfriend
Most of us get new neighbours now and then. Also me a forth-night ago. I never have had time to meet my neighbour. I've overheard her boyfriend very hairy and popular among students. He can speak. I worried.”
By setting the level of summarizing into maximum, 100%, the algorithm brings the following result compared to the original text:
”Neighbour's Hairy Boyfriend
Vuf.”
As we have now clearly demonstrated, can Text Summariser assembled with a proper algorithm result the most important content of the text very briefly, which can be an advantage to scientist, so they do not have to read so much.
Of applying the UK-HM algorithm the result could be even further summarised; By replacing The Base-A algorithm with UK-HM, string is can be summarised into the most shortest form, what we scientist call, the Royal form: “Vuf”. However, due to some non commercial restrictions we not able to use it and we will have accept a bit longer form for the public access.
Prof. Jorgen Lordish
Department of Politics and Terrorism
Malmö Tekniska Universität
Category: Comedy news, partly true, mostly nonsense.
Prof. Jorgen Lordish
e-mail:
jorgen.lordish@edu.mtu.se
