The aim of automatic text summarization (ATS) is to extract representative texts from documents and keep major points of the extracted texts consistent with the original documents. However, most existing studies ignore sentimental information loss in the summarization process, which leads to sentiment loss summarization. To address the sentiment loss issue during summarization, we introduce a sentiment compensation mechanism into document summarization and propose a graph-based extractive summarization approach named Sentiment Lossless Summarization (SLS). SLS first creates a graph representation for a document to obtain the importance score (i.e., literal indicator) of each sentence. Second, sentiment dictionaries are leveraged to analyze the sentence sentiments. Third, during each summarization iteration, the sentences with the lowest scores are iteratively removed, and the sentiment compensation weights of the remaining sentences are updated. With the help of sentiment compensation during the summarization process, sentiment consistencies between candidate summaries and the original documents are maintained. Intrinsic evaluations conducted on the DUC2001, DUC2002, DUC2004, and Multi-News datasets demonstrate that our approach outperforms baselines and state-of-the-art summarization methods in terms of Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores. Additionally, to further evaluate SLS performance in sentiment retention, extrinsic evaluations are introduced, and summary quality in terms of sentiment loss is evaluated by measuring the prediction accuracy for sentiment polarities of either movie (IMDb dataset) or product (Amazon dataset) review summaries. The experimental results demonstrate that our approach can improve prediction accuracy by at most 6% compared to the baseline.

Sentiment Lossless Summarization

Abstractive summarization is challenging problem, especially abstractive summarization based on unsupervised learning because it must generate whole, unique sentences. In the real world, companies use abstractive summarization to understand customer feedbacks. In many cases, this work is done by humans and so is expensive in terms of time and money. Therefore, there is an increasing demand for machine learning-based abstractive summarization systems. However, most previous abstractive summarization studies were of supervised models. In this paper, we proposed novel abstractive summarization methods that can be trained unsupervisedly. One of the proposed methods is based on Adversarially Regularized Autoencoder(ARAE) model, but abstractive summary generation method for each cluster of similar customers’ reviews, is newly proposed. We further proposed Conditional Adversarially Regularized Autoencoder(CARAE) model which is similar to the ARAE model but with the addition of condition nodes so that additional information about the cluster can be used during summarization. We first performed summary experiments based on Korean and additionally performed experiments on English. In the experiments, we set up some comparison models and used ROUGE and BLEU to evaluate our proposed models’ performance. Overall, our proposed models outperformed the comparison models and CARAE model performed better than the ARAE model.

Generating summary sentences using Adversarially Regularized Autoencoders with conditional context

Automatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the summary will be used. Nonetheless, there are special cases where it is not found enough textual sources, and one possible alternative is to generate a summary from a different domain. One manner to summarize texts consists of using a graph model. This model allows giving more importance to words corresponding to the main concepts from the target domain found in the summarized text. This gives the reader an overview of the main text concepts as well as their relationships. However, this kind of summarization presents a significant number of repeated terms when compared to human-generated summaries. In this paper, we present an approach to produce graph-model extractive summaries of texts, meeting the target domain exigences and treating the terms repetition problem. To evaluate the proposition, we performed a series of experiments showing that the proposed approach statistically improves the performance of a model based on Graph Centrality, achieving better coverage, accuracy, and recall.

Modeling, comprehending and summarizing textual content by graphs.

With the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a diversified review summary that focus on coverage and diversity a major goal. Most review summarization work focuses on showing salient reviews as a summary which might ignore diversity in summaries. In this paper, we present a graph-based algorithm that is capable of producing extractive summaries that are both diversified from a sentiment point of view and topically well-covered. First, we use statistical measures to find topical words. Then we split the dataset based on the sentiment class of the reviews and perform the ranking on each sentiment graph. When compared with different baselines, our approach scores best in most ROUGE metrics. Specifically, our approach shows improvements of 3.9% in ROUGE-1 and 1.8% in ROUGE-L in comparison with the best competing baseline.

Abeer Al-Suhaim

Papers

Sentiment diversification for short review summarization