Topic
Multi-document summarization
About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.
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31 Mar 2008
TL;DR: A framework of an automatic summary generation of one specific domain that is oil palm literature is described, based on two different paradigms which is extraction and abstraction, and the oil palm corpus is developed.
Abstract: Most of the existing summarization tools serve as a general purpose summarizer, rarely as the domain specific summarizer; e.g.: medical [14] and law [15] field documents summarizer. This paper describes a framework of an automatic summary generation of one specific domain that is oil palm literature. In order to support the whole framework, the oil palm corpus is developed. The work is based on two different paradigms which is extraction and abstraction. By incorporating these two important methods in one summarization framework, the quality of the produced summary will greatly improve. A Nearly-New IE (ANNIE) is used as the backbone in extraction process. The sentences are then ranked for potential inclusion in the summary using a weighted word frequency known as Term Frequency-Inverse Document Frequency (TF-IDF). In the abstraction process, the oil palm corpus is used to support the summarization procedure. Using the training corpus, the output will be more precise may gather all the important facts from the pre-determined information retrieval process.
TL;DR: Through two dreams, past and current, an ideal online information retrieval system is depicted, including full text online access, real time reference assistance via the Internet, and automatic summarization of all papers and chapters.
Abstract: Through two dreams, past and current, an ideal online
information retrieval system is depicted, including full text online
access, real time reference assistance via the Internet, and automatic
summarization of all papers and chapters. This is the near future of
information retrieval and processing. Selected resources on automatic
summarization are reported and some thoughts on implications are
offered.
TL;DR: Automatic summarization is the act of computationally condensing a set of data to produce a subset (a summary) that captures the key ideas or information within the original text as discussed by the authors .
Abstract: Automatic summarization is the act of computationally condensing a set of data to produce a subset (a summary) that captures the key ideas or information within the original text. To do this, artificial intelligence algorithms that are tailored for diverse sorts of data are frequently created and used. Ten research articles considering databases like IEEE, Scopus, and Springer Nature have been considered. The paradigm shift that AI has created in the field of Automatic Text Summarization is discussed in detail.
TL;DR: This paper discusses on extractive text summarization using sentence scoring and sentence ranking method, which reduces the time required for reading whole document and also it reduces space that is needed for storing large amount of data.
Abstract: In this fast paced technological era, where huge quantity of information is generating on the internet day by day. Since the dotcom bubble burst back in 2000, technology has radically transformed our societies. So, it is necessary to provide the better mechanism to extract the useful information fast and most effectively. Automatic text summarization is one of the methods of identifying the important meaningful information in a document or set related document and compressing them into a shorter version preserving its overall meanings. It reduces the time required for reading whole document and also it reduces space that is needed for storing large amount of data. Automatic Text summarization has two approaches 1) Abstractive text summarization and 2) Extractive text summarization. In extractive text summarization only important information or sentence are extracted from the given text file or original document. Here we will discuss on extractive text summarization using sentence scoring and sentence ranking method.
TL;DR: In this paper , the authors proposed an extractive Arabic review summarization approach that incorporates the reviews' polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank.
Abstract: The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.