Due to the increasing number of text-based content sources, demand for effective sentiment analysis and document summarization techniques has been increasing. Several transformer-based models, including “ELECTRA, BERT, XLNet, RoBERTa, DistilBERT, ALBERT” have emerged as promising alternatives traditional methods. This paper aims study effectiveness different transformer models performing on Yel...

This paper presents our extractive summarization systems at the update summarization track of TAC 2009. This system is based on our newly developed document summarization framework under the theory of conditional information distance among many objects. The best summary is defined in this paper to be the one which has the minimum information distance to the entire document set. The best update ...

This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization—a “parse-and-trim” approach and a statistical noisy-channel approach. We introduce the Multi-Candidate Reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates a...

Summarization of multiple documents featuring multiple topics is discussed. The example trea.ted here consists of fifty articles about the Peru hostage incident tbr ])ecember 1996 through April 1997. They include a. lot of topics such as opening, negotiation, ending, and so on. The method proposed in this paper is based on spreading activation over documents syntactically and semantically annot...

The primary focus of this paper is multi-document comparative summarization. At first, the concept of comparative summarization is defined, and then the existing approaches are described. Finally, a new method using LSA (Latent Semantic Analysis) for comparative summarization is proposed. Key-Words: Comparative summarization, contrastive summarization, summarization via LSA

In this paper we describe a new method of automatic summarization based on a learning step to identify criteria that maximize the correlation between human summary and peer extract. The proposed method uses a genetic algorithm to produce extracts from a collection of source documents describing the same event. Theses extracts are compared to human summaries using “Rouge measure” in order to ide...