Story Summarization Using a Question-Answering Approach

2021 ◽  
pp. 46-69
Author(s):  
Sanah Nashir Sayyed ◽  
Namrata Mahender C.
Keyword(s):  
Statistical Methods ◽  
Question Answering ◽  
Text Summarization ◽  
Loss Of Information ◽  
Representative Data ◽  
Minimal Loss ◽  
Speech Data ◽  
Linguistic Methods ◽  
The Given ◽  
Text Images

Summarization is the process of selecting representative data to produce a reduced version of the given data with a minimal loss of information; so, it generally works on text, images, videos, and speech data. The chapter deals with not only concepts of text summarization (types, stages, issues, and criteria) but also with applications. The two main categories of approaches generally used in text summaries (i.e., abstractive and extractive) are discussed. Abstractive techniques use linguistic methods to interpret the text; they produce understandable and semantically equivalent sentences with a shorter length. Extractive techniques mostly rely on statistical methods for extracting essential sentences from the given text. In addition, the authors explore the SACAS model to exemplify the process of summarization. The SACAS system analyzed 50 stories, and its evaluation is presented in terms of a new measurement based on question-answering MOS, which is also introduced in this chapter.

scholarly journals Question-aware memory network for multi-hop question answering in human–robot interaction

2021 ◽  
Author(s):  
Xinmeng Li ◽  
Mamoun Alazab ◽  
Qian Li ◽  
Keping Yu ◽  
Quanjun Yin
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Knowledge Graph ◽  
Robot Interaction ◽  
Natural Language Question ◽  
Memory Network ◽  
The Given ◽  
Fine Tune ◽  
Language Question

AbstractKnowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

Experiments on Applying a Text Summarization System for Question Answering

2007 ◽  
pp. 372-376
Author(s):  
Pedro Paulo Balage Filho ◽  
Vinícius Rodrigues de Uzêda ◽  
Thiago Alexandre Salgueiro Pardo ◽  
Maria das Graças Volpe Nunes
Keyword(s):  
Question Answering ◽  
Text Summarization ◽  
Summarization System

scholarly journals Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8658-8665 ◽  
Author(s):  
Xiangpeng Li ◽  
Jingkuan Song ◽  
Lianli Gao ◽  
Xianglong Liu ◽  
Wenbing Huang ◽  
...  
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Computation Time ◽  
Comparable Result ◽  
Video Encoding ◽  
Visual Question Answering ◽  
Proposed Model ◽  
Ablation Study ◽  
The Given ◽  
Video Question Answering

Most of the recent progresses on visual question answering are based on recurrent neural networks (RNNs) with attention. Despite the success, these models are often timeconsuming and having difficulties in modeling long range dependencies due to the sequential nature of RNNs. We propose a new architecture, Positional Self-Attention with Coattention (PSAC), which does not require RNNs for video question answering. Specifically, inspired by the success of self-attention in machine translation task, we propose a Positional Self-Attention to calculate the response at each position by attending to all positions within the same sequence, and then add representations of absolute positions. Therefore, PSAC can exploit the global dependencies of question and temporal information in the video, and make the process of question and video encoding executed in parallel. Furthermore, in addition to attending to the video features relevant to the given questions (i.e., video attention), we utilize the co-attention mechanism by simultaneously modeling “what words to listen to” (question attention). To the best of our knowledge, this is the first work of replacing RNNs with selfattention for the task of visual question answering. Experimental results of four tasks on the benchmark dataset show that our model significantly outperforms the state-of-the-art on three tasks and attains comparable result on the Count task. Our model requires less computation time and achieves better performance compared with the RNNs-based methods. Additional ablation study demonstrates the effect of each component of our proposed model.

scholarly journals Use of Statistical Methods in Quality Control

2013 ◽  
Vol 16 (2) ◽  
pp. 35-38
Author(s):  
Miroslav Prístavka ◽  
Marián Bujna
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Statistical Methods ◽  
Management System ◽  
Quality Management System ◽  
Iso 9001 ◽  
Main Task ◽  
Production Problem ◽  
Use Of Knowledge ◽  
The Given

Abstract More and more organisations are beginning to realise how important is it to implement a quality management system. Its main task is to rejuvenate the given processes in production. Problem solving is systemic within quality assurance procedures of an organisation. This work describes a quality management system according to ISO 9001 and statistical methods in quality management. The theoretical part contains the characteristics and description of the system listed above. The practical part shows the use of knowledge in the organisation to solve problems.

scholarly journals Video Question Answering via Hierarchical Spatio-Temporal Attention Networks

2017 ◽  
Author(s):  
Zhou Zhao ◽  
Qifan Yang ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Question Answering ◽  
Temporal Dynamics ◽  
Temporal Attention ◽  
Attention Networks ◽  
Learning Framework ◽  
Spatio Temporal ◽  
The Given ◽  
Video Question Answering

Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoder-decoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the encoder-decoder learning method with reasoning recurrent neural networks for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.

scholarly journals Convolutional recurrent neural network with template based representation for complex question answering

2020 ◽  
Vol 10 (3) ◽  
pp. 2710
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Internal Representation ◽  
Semantic Matching ◽  
Softmax Classifier ◽  
Questions And Answers ◽  
Fully Connected ◽  
The Given ◽  
Complex Question

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

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