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...

summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals— mathematical objects with a high degree of redundancy. In this article, w...

Multi-document summarization (MDS) systems have been designed for short, unstructured summaries of 10-15 documents, and are inadequate for larger document collections. We propose a new approach to scaling up summarization called hierarchical summarization, and present the first implemented system, SUMMA. SUMMA produces a hierarchy of relatively short summaries, in which the top level provides a...

Since the late 50’s and Luhn [Luh58] the information community has expressed its interest in summarizing texts. The domains of application of such methodologies are countless, ranging from news summarization [WL03, BM05, ROWBG05] to scientific article summarization [TM02] and meeting summarization [NPDP05, ELH03]. Summarization has been defined as a reductive transformation of a given set of te...

The rapid growth of online information services has created the problem of information explosion. Automatic text summarization techniques are essential for dealing with this problem. The process of compacting a source document to reduce its complexity and length while retaining its most important contents is called text summarization. This paper introduces Parsumist-a text summarization system ...

Multi-document summarization (MDS) is an effective tool for information aggregation that generates informative and concise summary from a cluster of topic-related documents. Our survey, the first its kind, systematically overviews recent deep-learning-based MDS models. We propose novel taxonomy to summarize design strategies neural networks conduct comprehensive state art. highlight differences...

The trend toward the growing multilinguality of the Internet requires text summarization techniques that work equally well in multiple languages. Only some of the automated summarization methods proposed in the literature, however, can be defined as “languageindependent”, as they are not based on any morphological analysis of the summarized text. In this paper, we perform an in-depth comparativ...