tree-lstm summarization
195069474
Automatic Source Code Summarization with Extended Tree LSTM
Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short Term Memory (LSTM) can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially structured, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code.Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree LSTM is proposed as a generalization of LSTMs for tree structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree LSTM, which we call Multi way Tree LSTM and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state of the art techniques.
2019 International Joint Conference on Neural Networks (IJCNN)
Yusuke Shido Yasuaki Kobayashi Akihiro Yamamoto Atsushi Miyamoto Tadayuki Matsumura
51989705
An Attention Based Syntax Tree and Tree LSTM Model for Sentence Summarization
Generative Summarization is of great importance in understanding large scale textual data. In this work, we propose an attention based Tree LSTM model for sentence summarization, which utilizes an attention based syntactic structure as auxiliary information. Thereinto, block alignment is used to align the input and output syntax blocks, while inter alignment is used for alignment of words within that of block pairs. To some extent, block alignment can prevent structural deviations on the long sentences and inter alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end to end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state of art on DUC 2004 shared task.
Wenfeng Liu Peiyu Liu Yuzhen Yang Yaling Gao Jing Yi
27617079
Right to left LSTM Summarization Multitask Encoder Decoder Dependency Trees Right to left LSTM Summarization
In this work, we present a compact, modular framework for constructing new recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU) In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence tosequence, attention mechanisms, and recursive tree structured models. A TBRU can also serve as both an encoder for downstream tasks and as a decoder for its own task simultaneously, resulting in more accurate multi task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi task learning for extractive summarization tasks.
Chris Alberti Daniel Andor
232233271
Improving Code Summarization with Block wise Abstract Syntax Tree Splitting
Automatic code summarization frees software developers from the heavy burden of manual commenting and benefits software development and maintenance. Abstract Syntax Tree (AST) which depicts the source code's syntactic structure, has been incorporated to guide the generation of code summaries. However, existing AST based methods suffer from the difficulty of training and generate inadequate code summaries. In this paper, we present the Block wise Abstract Syntax Tree Splitting method (BASTS for short) which fully utilizes the rich tree form syntax structure in ASTs, for improving code summarization. BASTS splits the code of a method based on the blocks in the dominator tree of the Control Flow Graph, and generates a split AST for each code split. Each split AST is then modeled by a Tree LSTM using a pre training strategy to capture local non linear syntax encoding. The learned syntax encoding is combined with code encoding, and fed into Transformer to generate high quality code summaries. Comprehensive experiments on benchmarks have demonstrated that BASTS significantly outperforms state of the art approaches in terms of various evaluation metrics. To facilitate reproducibility, our implementation is available at https:/github.com/XMUDM/BASTS.
2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)
Zhi-bin Ouyang Junqing Zhuang Jianqiang Chen Rongxin Wu
207757424
Going Beyond Content Richness: Verified Information Aware Summarization of Crisis Related Microblogs
High impact catastrophic events (bomb attacks, shootings) trigger posting of large volume of information on social media platforms such as Twitter. Recent works have proposed content aware systems for summarizing this information, thereby facilitating post disaster services. However, a significant proportion of the posted content is unverified, which restricts the practical usage of the existing summarization systems. In this paper, we work on the novel task of generating verified summaries of information posted on Twitter during disasters. We first jointly learn representations of content classes and expression classes of tweets posted during disasters using a novel LDA based generative model. These representations of content expression classes are used in conjunction with pre disaster user behavior and temporal signals (replies) for training a Tree LSTM based tweet verification model. The model infers tweet verification probabilities which are used, besides information content of tweets, in an Integer Linear Programming (ILP) framework for generating the desired verified summaries. The summaries are fine tuned using the class information of the tweets as obtained from the LDA based generative model. Extensive experiments are performed on a publicly available labeled dataset of man made disasters which demonstrate the effectiveness of our tweet verification (3 13% gain over baselines) and summarization (12 48% gain in verified content proportion, 8 13% gain in ROUGE score over state of the art) systems. We make implementations of our various modules available online.
CIKM
Ashish Sharma Koustav Rudra Niloy Ganguly
153313525
Capturing source code semantics via tree based convolution over API enhanced AST
When deep learning meets big code, a key question is how to efficiently learn a distributed representation for source code that can capture its semantics effectively. We propose to use tree based convolution over API enhanced AST. To demonstrate the effectiveness of our approach, we apply it to detect semantic clones code fragments with similar semantics but dissimilar syntax. Experiment results show that our approach outperforms an existing state of the art approach that uses tree based LSTM, with an increase of 0.39 and 0.12 in F1 score on OJClone and BigCloneBench respectively. We further propose architectures that incorporate our approach for code search and code summarization.
CF
Long Chen Wei Ye Shikun Zhang
27053218
Object Triggered Egocentric Video Summarization
Egocentric videos are usually of long duration and contains lot of redundancy which makes summarization an essential task for such videos. In this work we are targeting object triggered egocentric video summarization which aims at extracting all the occurrences of an object in a given video, in near real time. We propose a modular pipeline which first aims at limiting the redundant information and then uses a Convolutional Neural Network and LSTM based approach for object detection. Following this we represent the video as a dictionary which captures the semantic information in the video. Matching a query object reduces to doing an And Or Tree traversal followed by deepmatching algorithm for fine grained matching. The frames containing the object, which would have been missed at the pruning stage are retrieved by running a tracker on the frames selected by the pipeline mentioned. The modular pipeline allows replacing any module with its more efficient version. Performance tests ran on the overall pipeline for egocentric datasets, EDUB dataset and personal recorded videos, give an average recall of 0.76.
CAIP
Samriddhi Jain Renu M Rameshan Aditya Nigam
195346482
Query2Vec: NLP Meets Databases for Generalized Workload Analytics
We propose methods for learning vector representations of SQL workloads to support a variety of administration tasks and application features, including query recommendation, workload summarization, index selection, identifying expensive queries, and predicting query reuse. We consider vector representations of both raw SQL text and optimized query plans under various assumptions and pre processing strategies, and evaluate these methods on multiple real SQL workloads by comparing with results of task and application feature metrics in the literature. We find that simple algorithms based on these generic vector representations compete favorably with previous approaches that require a number of assumptions and task specific heuristics. We then present a new embedding strategy specialized for queries based on tree structured Long Short Term Memory (LSTM) network architectures that improves on the text oriented embeddings for some tasks. We find that the general approach, when trained on a large corpus of SQL queries, provides a robust foundation for a variety of workload analysis tasks. We conclude by considering how workload embeddings can be deployed as a core database system feature to support database maintenance and novel applications.
ArXiv
Shrainik Jain Bill Howe
210994766
Make It Directly: Event Extraction Based on Tree LSTM and Bi GRU
Event extraction is an important research direction in the field of natural language processing (NLP) applications including information retrieval (IR) Traditional event extraction is realized with two methods: the pipeline and the joint extraction methods. The pipeline method determines the event by triggering word recognition to further implement event extraction and is prone to error cascading. The joint extraction method applies deep learning to implement the completion of the trigger word and the argument role classification task. Most studies with the joint extraction method adopt the CNN or RNN network structure. However, in the case of event extraction, deeper understanding of complex contexts is required. Existing studies do not make full use of syntactic relations. This paper proposes a novel event extraction model, which is built upon a Tree LSTM network and a Bi GRU network and carries syntactically related information. It is illustrated that this method simultaneously uses Tree LSTM and Bi GRU to obtain a representation of the candidate event sentence and identify the event type, which results in a better performance compared to the ones that use chain structured LSTM, CNN or only Tree LSTM. Finally, the hidden state of each node is used in Tree LSTM to predict a label for candidate arguments and identify/classify all arguments of an event. Lab results show that the proposed event extraction model achieves competitive results compared to previous works.
IEEE Access
Wentao Yu Mianzhu Yi Xiaohui Huang Xiao Yi Qingjun Yuan
213986424
Indonesian Short Essay Scoring Using Transfer Learning Dependency Tree LSTM
Distributed representation of a sentences cannot only be seen with the sequence but also with dependency. In this paper, we proposed an answer assessment model that considers with dependency relational each word. The dependency relational is obtained by universal dependency modelling from CoNLL format data. The dependency relational is used in Long Short Term Memory (LSTM) architecture by modified the hidden state, which is called dependency tree LSTM. The proposed method has an improvement on QWK and accuracy in 2.38% and 2.05% respectively, that compared with the LSTM state of the art in the English short essay. Furthermore, the proposed method with an Indonesian short essay shows the evaluation of QWK and accuracy of 68.07% and 82.51% respectively.
Agung Wiratmo Chastine Fatichah