In this paper, a new summarization system is proposed, which summarizes a document by interactively scoring the sentences using already-extracted summary so that the sen-tence which contains the most amount of relevant informa-tion to the summary to be extracted. To calculate the amount of relevant information contained by a sentence, the system makes heavy use of already existing taxonomies. The experiment shows a result pretty close to the current state-of-the-art systems
Abstract:- In this paper, we propose a practical approach for extracting the most relevant sentences...
This paper investigates on sentence extraction based single Document summarization. It saves time in...
This paper proposes an extractive generic text summarization model that generates summaries by selec...
The need for text summarization is crucial as we enter the era of information overload. In this pape...
This paper describes a query-relevant text summary system based on interactive learning. The system ...
In text summarization, relevance and coverage are two main criteria that decide the quality of a sum...
As the information on the internet continues to expand exponentially, machine learning, is becoming ...
This paper describes a multidocument summarizer built upon re-search into the detection of new infor...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
Automatic text summarization, the reduction of a text to its essential content is fundamental for an...
Seeking bits of useful information from a large amount of data on the Web still remains a difficult ...
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input ...
The task of automatic document summarization aims at generating short summaries for originally long ...
Abstract—Many previous research studies on extractive text summarization consider a subset of words ...
We present a new system for custom summarizations of large text corpora at interactive speed. The ta...
Abstract:- In this paper, we propose a practical approach for extracting the most relevant sentences...
This paper investigates on sentence extraction based single Document summarization. It saves time in...
This paper proposes an extractive generic text summarization model that generates summaries by selec...
The need for text summarization is crucial as we enter the era of information overload. In this pape...
This paper describes a query-relevant text summary system based on interactive learning. The system ...
In text summarization, relevance and coverage are two main criteria that decide the quality of a sum...
As the information on the internet continues to expand exponentially, machine learning, is becoming ...
This paper describes a multidocument summarizer built upon re-search into the detection of new infor...
Automatic summarization is the process of presenting the contents of written documents in a short, c...
Automatic text summarization, the reduction of a text to its essential content is fundamental for an...
Seeking bits of useful information from a large amount of data on the Web still remains a difficult ...
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input ...
The task of automatic document summarization aims at generating short summaries for originally long ...
Abstract—Many previous research studies on extractive text summarization consider a subset of words ...
We present a new system for custom summarizations of large text corpora at interactive speed. The ta...
Abstract:- In this paper, we propose a practical approach for extracting the most relevant sentences...
This paper investigates on sentence extraction based single Document summarization. It saves time in...
This paper proposes an extractive generic text summarization model that generates summaries by selec...