scholarly journals Skeleton to Abstraction: An Attentive Information Extraction Schema for Enhancing the Saliency of Text Summarization

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 217 ◽  
Author(s):  
Xiujuan Xiang ◽  
Guangluan Xu ◽  
Xingyu Fu ◽  
Yang Wei ◽  
Li Jin ◽  
...  
Keyword(s):  
Information Extraction ◽  
Full Text ◽  
State Of The Art ◽  
Irrelevant Information ◽  
Source Text ◽  
Daily Mail ◽  
Human Evaluation ◽  
Proposed Model ◽  
Abstractive Summarization ◽  
Extraction Model

Current popular abstractive summarization is based on an attentional encoder-decoder framework. Based on the architecture, the decoder generates a summary according to the full text that often results in the decoder being interfered by some irrelevant information, thereby causing the generated summaries to suffer from low saliency. Besides, we have observed the process of people writing summaries and find that they write a summary based on the necessary information rather than the full text. Thus, in order to enhance the saliency of the abstractive summarization, we propose an attentive information extraction model. It consists of a multi-layer perceptron (MLP) gated unit that pays more attention to the important information of the source text and a similarity module to encourage high similarity between the reference summary and the important information. Before the summary decoder, the MLP and the similarity module work together to extract the important information for the decoder, thus obtaining the skeleton of the source text. This effectively reduces the interference of irrelevant information to the decoder, therefore improving the saliency of the summary. Our proposed model was tested on CNN/Daily Mail and DUC-2004 datasets, and achieved a 42.01 ROUGE-1 f-score and 33.94 ROUGE-1, recall respectively. The result outperforms the state-of-the-art abstractive model on the same dataset. In addition, by subjective human evaluation, the saliency of the generated summaries was further enhanced.

scholarly journals Learning Personalized End-to-End Goal-Oriented Dialog

2019 ◽  
Vol 33 ◽  
pp. 6794-6801 ◽  
Author(s):  
Liangchen Luo ◽  
Wenhao Huang ◽  
Qi Zeng ◽  
Zaiqing Nie ◽  
Xu Sun
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Preferences ◽  
Task Completion ◽  
Dialog Systems ◽  
Performance Improvements ◽  
Human Evaluation ◽  
Proposed Model ◽  
End To End ◽  
Qualitative Performance

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

scholarly journals Diverse Decoding for Abstractive Document Summarization

2019 ◽  
Vol 9 (3) ◽  
pp. 386 ◽  
Author(s):  
Xu-Wang Han ◽  
Hai-Tao Zheng ◽  
Jin-Yuan Chen ◽  
Cong-Zhi Zhao
Keyword(s):  
Experimental Evaluation ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Beam Search ◽  
Daily Mail ◽  
Document Summarization ◽  
Novel Method ◽  
Search Approach ◽  
Abstractive Summarization ◽  
Information Coverage

Recently, neural sequence-to-sequence models have made impressive progress in abstractive document summarization. Unfortunately, as neural abstractive summarization research is in a primitive stage, the performance of these models is still far from ideal. In this paper, we propose a novel method called Neural Abstractive Summarization with Diverse Decoding (NASDD). This method augments the standard attentional sequence-to-sequence model in two aspects. First, we introduce a diversity-promoting beam search approach in the decoding process, which alleviates the serious diversity issue caused by standard beam search and hence increases the possibility of generating summary sequences that are more informative. Second, we creatively utilize the attention mechanism combined with the key information of the input document as an estimation of the salient information coverage, which aids in finding the optimal summary sequence. We carry out the experimental evaluation with state-of-the-art methods on the CNN/Daily Mail summarization dataset, and the results demonstrate the superiority of our proposed method.

scholarly journals Biomedical document triage using a hierarchical attention-based capsule network

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Jian Wang ◽  
Mengying Li ◽  
Qishuai Diao ◽  
Hongfei Lin ◽  
Zhihao Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Information Extraction ◽  
Precision Medicine ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Experimental Results ◽  
Biomedical Domain ◽  
Proposed Model ◽  
Document Triage

Abstract Background Biomedical document triage is the foundation of biomedical information extraction, which is important to precision medicine. Recently, some neural networks-based methods have been proposed to classify biomedical documents automatically. In the biomedical domain, documents are often very long and often contain very complicated sentences. However, the current methods still find it difficult to capture important features across sentences. Results In this paper, we propose a hierarchical attention-based capsule model for biomedical document triage. The proposed model effectively employs hierarchical attention mechanism and capsule networks to capture valuable features across sentences and construct a final latent feature representation for a document. We evaluated our model on three public corpora. Conclusions Experimental results showed that both hierarchical attention mechanism and capsule networks are helpful in biomedical document triage task. Our method proved itself highly competitive or superior compared with other state-of-the-art methods.

CATS: Customizable Abstractive Topic-based Summarization

2022 ◽  
Vol 40 (1) ◽  
pp. 1-24
Author(s):  
Seyed Ali Bahrainian ◽  
George Zerveas ◽  
Fabio Crestani ◽  
Carsten Eickhoff
Keyword(s):  
Computer Science ◽  
State Of The Art ◽  
Original Text ◽  
Learning Method ◽  
Source Text ◽  
Resource Setting ◽  
Low Resource Setting ◽  
Topic Distribution ◽  
Latent Topic ◽  
Abstractive Summarization

Neural sequence-to-sequence models are the state-of-the-art approach used in abstractive summarization of textual documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, custom generation of summaries (e.g., towards a user’s preference) remains unexplored. In this article, we present CATS, an abstractive neural summarization model that summarizes content in a sequence-to-sequence fashion while also introducing a new mechanism to control the underlying latent topic distribution of the produced summaries. We empirically illustrate the efficacy of our model in producing customized summaries and present findings that facilitate the design of such systems. We use the well-known CNN/DailyMail dataset to evaluate our model. Furthermore, we present a transfer-learning method and demonstrate the effectiveness of our approach in a low resource setting, i.e., abstractive summarization of meetings minutes, where combining the main available meetings’ transcripts datasets, AMI and International Computer Science Institute(ICSI) , results in merely a few hundred training documents.

scholarly journals RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

2020 ◽  
Author(s):  
Hanning Gao ◽  
Lingfei Wu ◽  
Po Hu ◽  
Fangli Xu
Keyword(s):  
State Of The Art ◽  
Global Structure ◽  
The State ◽  
Experimental Results ◽  
Text Generation ◽  
Structure Information ◽  
Text Quality ◽  
Human Evaluation ◽  
Proposed Model

The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.

scholarly journals A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization

2018 ◽  
Author(s):  
Li Wang ◽  
Junlin Yao ◽  
Yunzhe Tao ◽  
Li Zhong ◽  
Wei Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Text Summarization ◽  
The Other ◽  
Learning Approach ◽  
Automatic Summarization ◽  
Word Level ◽  
Proposed Model ◽  
Abstractive Summarization

In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, our approach can improve coherence, diversity, and informativeness of generated summaries via a biased probability generation mechanism. On the other hand, reinforcement training, like SCST, directly optimizes the proposed model with respect to the non-differentiable metric ROUGE, which also avoids the exposure bias during inference. We carry out the experimental evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization.

scholarly journals Image Captioning with Compositional Neural Module Networks

2019 ◽  
Author(s):  
Junjiao Tian ◽  
Jean Oh
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Input Image ◽  
Amazon Mechanical Turk ◽  
Image Captioning ◽  
Hierarchical Framework ◽  
Human Evaluation ◽  
Proposed Model ◽  
N Gram ◽  
Sequential Models

In image captioning where fluency is an important factor in evaluation, n-gram metrics, sequential models are commonly used; however, sequential models generally result in overgeneralized expressions that lack the details that may be present in an input image. Inspired by the idea of the compositional neural module networks in the visual question answering task, we introduce a hierarchical framework for image captioning that explores both compositionality and sequentiality of natural language. Our algorithm learns to compose a detail-rich sentence by selectively attending to different modules corresponding to unique aspects of each object detected in an input image to include specific descriptions such as counts and color. In a set of experiments on the MSCOCO dataset, the proposed model outperforms a state-of-the art model across multiple evaluation metrics, more importantly, presenting visually interpretable results. Furthermore, the breakdown of subcategories f-scores of the SPICE metric and human evaluation on Amazon Mechanical Turk show that our compositional module networks effectively generate accurate and detailed captions.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

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