A novel concept-level approach for ultra-concise opinion summarization

Abstract

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users comunicate, exchange, and share opinions. The explotation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed.Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of stateof-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

Application of Different Algorithms to Optimize Abstractive Summarization

“…Abstractive text summarization using LSTM-CNN based deep learning [12]. 2018 Song, Shengli Huang, Haitao Ruan, Tongxiao [12].…”

“…First extracts phrases from source sentences and then generates text summaries using deep learning [12].…”

“…Opizer-E and Opizer-A [11] 10. LSTM-CNN based ATSDL model [12] 11. novel concept-level approach [13] Least entropy regularization hones the utilization of different words. This model is successful for accomplishing significant enhancements for interpretation execution and outline quality.…”

Abstractive Summarization with NLP Makes Easier to Memorize

“…A novel concept-level approach for ultra-concise opinion summarization [13] 2015 Lloret, Elena Boldrini, Ester Vodolazova, Tatiana Martinez-Barco, Patricio Munoz, Rafael Palomar, Manuel [13] novel concept-level approach [13] Simplification and a regeneration stages [13].…”

A Novel Framework for Semantic OrientedAbstractive Text Summarization

“…Lloret et al [30] built a model for generating ultra-concise concept-level summaries. The system initially converts the input document into its syntactic representation after lexical analysis.…”