In the area of text summarization, there have been significant advances recently. meantime, current trend in summarization is focused more on news summarization. Therefore, developing a synthesis approach capable extracting, comparing, and ranking sentences vital to create summary various articles context erroneous online data. It necessary, however, for system be able deal with multi-document ...

Major text summarization research is mainly focusing on summarizing short documents and very few works witnessed for long document summarization. Additionally, extractive more addressed as compared with abstractive Abstractive summarization, unlike does not only copy essential words from the original but requires paraphrasing to get close human generated summary. The machine learning, deep lear...

University of Ljubljana Faculty of Computer and Information Science Ercan Canhasi Graph-based models for multi-document summarization is thesis is about automatic document summarization, with experimental results on general, query, update and comparative multi-document summarization (MDS). We describe prior work and our own improvements on some important aspects of a summarization system, incl...

We present our state of the art multilingual text summarizer capable of single as well as multi-document text summarization. The algorithm is based on repeated application of TextRank on a sentence similarity graph, a bag of words model for sentence similarity and a number of linguistic preand post-processing steps using standard NLP tools. We submitted this algorithm for two different tasks of...

This paper describes a method for language independent extractive summarization that relies on iterative graph-based ranking algorithms. Through evaluations performed on a single-document summarization task for English and Portuguese, we show that the method performs equally well regardless of the language. Moreover, we show how a metasummarizer relying on a layered application of techniques fo...

Nowadays, people deal with a huge amount of data, especially while they are surfing on internet. So, this makes the topic of automatic summarization is very important and in the forefront. In this paper, a review for text summarization methods in Turkish is presented. Brief summary of the methods used for automatic text summarization in the literature, and also brief definitions of summary, abs...

The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In the paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling...

Due to a large variety of noisy information embedded in Web pages, Web-page classification is much more difficult than pure-text classification. In this paper, we propose to improve the Web-page classification performance by removing the noise through summarization techniques. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the perfor...

We present a novel system combination of machine translation and text summarization which provides high quality summary translations superior to the baseline translation of the entire document. We first use supervised learning and build a classifier that predicts if the translation of a sentence has high or low translation quality. This is a reference-free estimation of MT quality which helps u...

This paper reports recent advances in automatic speech summarization method. In our proposed method, a set of words maximizing a summarization score is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming technique. The extracted set of words is then connected to build a summarized sentence. The summari...