We propose a novel unsupervised extractive approach for summarizing online reviews by exploiting review helpfulness ratings. In addition to using the helpfulness ratings for review-level filtering, we suggest using them as the supervision of a topic model for sentence-level content scoring. The proposed method is metadata-driven, requiring no human annotation, and generalizable to different kin...

Text summarization is the process of taking a text document and creating a compressed version that consists of the most useful information for the user. One distinguishes between single-document summarizers (SDS) and multi-document summarizers (MDS). Multi-document summarization is much more complicated than single-document summarization. Factors that make multi-document summarization more diff...

The increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The proposed methods are based on the hierarchical combination of single-document summaries, and achieves state of the art results.

It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and n...

Purpose Summarization of an entire Web site with diverse content may lead to a summary heavily biased towards the site’s dominant topics. This paper presents a novel topic-based framework to address this problem. Design/methodology/approach A two-stage framework is proposed. The first stage identifies the main topics covered in a Web site via clustering and the second stage summarizes each topi...

Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology vari...

In this paper, we discuss techniques, algorithms, evaluation methods used in online, offline, supervised, unsupervised, multi-video and clustering for Video Summarization/Multi-view Summarization from various references. We have studied different techniques the literature described features generating video summaries with methods, algorithms datasets used. covered survey towards new frontier of...

The ever-increasing number of online lectures has created an unprecedented opportunity for distance learning. Most online lectures are presented as unstructured text, audio and/or video files which make it di cult for students to locate relevant lectures and browse through them. In this thesis, we investigated several automatic lecture segmentation and summarization algorithms. Automatic lectur...

OBJECTIVE The aim of this paper is to survey the recent work in medical documents summarization. BACKGROUND During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in...