automatic survey generation
129946307
HSDS: An Abstractive Model for Automatic Survey Generation
Automatic survey generation for a specific research area can quickly give researchers an overview, and help them recognize the technical developing trend of the specific area. As far as we know, the most relevant study with automatic survey generation is the task of automatic related work generation. Almost all existing methods of automatic related work generation extract the important sentences from multiple relevant papers to assemble a related work. However, the extractive methods are far from satisfactory because of poor coherence and readability. In this paper, we propose a novel abstractive method named Hierarchical Seq2seq model based on Dual Supervision (HSDS) to solve problems above. Given multiple scientific papers in the same research area as input, the model aims to generate a corresponding survey. Furthermore, we build a large dataset to train and evaluate the HSDS model. Extensive experiments demonstrate that our proposed model performs better than the state of the art baselines.
DASFAA
Xiao-Jian Jiang Xianling Mao Bo-Si Feng Xiaochi Wei Bin-Bin Bian Heyan Huang
68018059
CitationAS: A Tool of Automatic Survey Generation Based on Citation Content
Abstract Purpose This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles. Design/methodology/approach Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys. Findings In experiments, we choose 20 high frequency phrases as search terms. Results show that Lingo Word2Vec, STC WordNet and bisecting K means Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF IDF or MMR can acquire higher survey quality. Research limitations The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing. Practical implications CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user's search terms. It can also help researchers learn about research status in a certain field. Originality/value CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF IDF and MMR.
Jie Wang Chengzhi Zhang Mengying Zhang Sanhong Deng
220483684
AutoSurvey: Automatic Survey Generation based on a Research Draft
This work presents AutoSurvey, an intelligent system that performs literature survey and generates a summary specific to a research draft. A neural model for information structure analysis is employed for extracting fine grained information from the abstracts of previous work, and a novel evolutionary multi source summarization model is proposed for generating the summary of related work. This system is extremely used for both academic and educational purposes.
IJCAI
Hen-Hsen Huang
70335228
Automatic Multiple Choice Question Generation From Text: A Survey
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's. Since then, many systems have been developed for MCQ generation. We perform a systematic review of those systems. This paper presents our findings on the review. We outline a generic workflow for an automatic MCQ generation system. The workflow consists of six phases. For each of these phases, we find and discuss the list of techniques adopted in the literature. We also study the evaluation techniques for assessing the quality of the system generated MCQs. Finally, we identify the areas where the current research focus should be directed toward enriching the literature.
IEEE Transactions on Learning Technologies
Dhawaleswar Rao Ch Sujan Kumar Saha
218871925
A Survey of Automatic Generation of Code Comments
Code comments are a valuable form of documentation attached to code that is the most intuitive and efficient way for programmers to understand software code. Good code comments can help programmers quickly understand the role of source code and facilitate understanding of programs and software maintenance tasks. However, in practice, most programmers only pay attention to the code and ignore the comments and documents, which makes the program's readability and maintainability greatly reduced. Based on the meaning of code comments, this paper discusses the current progress in the field of code comments research, adopts the comparative analysis method, focuses on the classification research of the methods and tools for automatic generation of code comments, expounds its advantages and disadvantages, and reveals the issues that need further study.
ICMSS
Fengrong Zhao Junqi Zhao Yang Bai
214624357
Automatic Generation of Survey Paper Based on Template Tree
With rapid expansion of scientific papers, making a survey from a large collection of papers on a given research issue or domain becomes more and more important for researchers. This paper proposes a template based framework for automatically generating survey paper. It allows users to compose a template tree as a syllabus for the required survey. Each tree node corresponds to a section to be composed in the survey therefore the whole tree defines the section structure of the survey. The template consists of two types of nodes, dimension node and topic node, which filter contents of papers. A recursive procedure along the survey generation template tree paths is conducted to process documents, rank sentences, and compose sections. We apply the approach to generating the survey of the reference papers of a survey paper and compare the result with the survey paper. Experiments show improvement over several baseline methods.
2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
Xiaoping Sun Hai Zhuge
198336999
A Survey on Automatic Model Generation for Material Flow Simulation in Discrete Manufacturing
Abstract Adaptivity of production systems is acknowledged as a key competition factor in the manufacturing industry. In order to decrease reaction time and increase cost efficiency, changes to a production system are often planned and validated using material flow simulation. However, repeated manual synchronization of the simulation model with the real manufacturing environment is time consuming and error prone. Automatic simulation model generation (ASMG) could overcome these issues. In contrast to recent surveys on ASMG, we focus on information retrieval. Accordingly, we constitute the state of the art of this problem domain and outline future research directions.
Heiner Reinhardt Marek Weber Matthias Putz
198897945
A Survey of Automatic Generation of Source Code Comments: Algorithms and Techniques
As an integral part of source code files, code comments help improve program readability and comprehension. However, developers sometimes do not comment their program code adequately due to the incurred extra efforts, lack of relevant knowledge, unawareness of the importance of code commenting or some other factors. As a result, code comments can be inadequate, absent or even mismatched with source code, which affects the understanding, reusing and the maintenance of software. To solve these problems of code comments, researchers have been concerned with generating code comments automatically. In this work, we aim at conducting a survey of automatic code commenting researches. First, we generally analyze the challenges and research framework of automatic generation of program comments. Second, we present the classification of representative algorithms, the design principles, strengths and weaknesses of each category of algorithms. Meanwhile, we also provide an overview of the quality assessment of the generated comments. Finally, we summarize some future directions for advancing the techniques of automatic generation of code comments and the quality assessment of comments.
IEEE Access
Xiaotao Song Hailong Sun Xu Wang Jiafei Yan
201080710
Automatic Survey Template Generation Using Research Relations
(c) 2015 AENSI Publisher All rights reserved. To Cite This Article: G. Manju, S. Deepa, A. Gayathri, M. Suganya, T.V. Geetha. Automatic Survey Template Generation Using research Relations. Adv. in Nat. Appl. Sci. 9(6) 593 599, 2015
G Manju S N Deepa A Gayathri M Suganya T V Geetha
51613554
A survey on automatic image caption generation
Abstract Image captioning means automatically generating a caption for an image. As a recently emerged research area, it is attracting more and more attention. To achieve the goal of image captioning, semantic information of images needs to be captured and expressed in natural languages. Connecting both research communities of computer vision and natural language processing, image captioning is a quite challenging task. Various approaches have been proposed to solve this problem. In this paper, we present a survey on advances in image captioning research. Based on the technique adopted, we classify image captioning approaches into different categories. Representative methods in each category are summarized, and their strengths and limitations are talked about. In this paper, we first discuss methods used in early work which are mainly retrieval and template based. Then, we focus our main attention on neural network based methods, which give state of the art results. Neural network based methods are further divided into subcategories based on the specific framework they use. Each subcategory of neural network based methods are discussed in detail. After that, state of the art methods are compared on benchmark datasets. Following that, discussions on future research directions are presented.
Neurocomputing
Shuang Bai Shan An