The need for text summarization is crucial as we enter the era of information overload. However, the current implementations are specific to a domain or a genre of the source document. In this paper, we discuss an algorithm for text summarization which is independent of domain and document source. This algorithm creates text summaries by analyzing the logical structure of the sentences. Sentenc...

MUltilingual Sentence Extractor (MUSE) is aimed at multilingual single-document summarization. MUSE implements a supervised language-independent summarization approach based on optimization of multiple sentence ranking methods using a Genetic Algorithm. The main advantage of MUSE is its language-independency – it is using statistical sentence features, which can be calculated for sentences in a...

NeATS is a multi-document summarization system that attempts to extract relevant or interesting portions from a set of documents about some topic and present them in coherent order. NeATS is among the best performers in the large scale summarization evaluat ion DUC 2001.

Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. T...

Multi document summarization has very great impact among research community, ever since the growth of online information and availability. Selecting most important sentences from such huge repository of data is quiet tricky and challenging task. While multi document poses some additional overhead in sentence selection, generating summaries for each individual documents and merging the sentences...

Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for si...

In this article, we explore an event detection framework to improve multi-document summarization. Our approach is based on a two-stage single-document method that extracts a collection of key phrases, which are then used in a centrality-as-relevance passage retrieval model. We explore how to adapt this singledocument method for multi-document summarization methods that are able to use event inf...