Novel multi‐domain attention for abstractive summarisation

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small perspective.In order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this work.First,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence level.Furthermore,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and sen-tences.Then,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary content.Finally,experiment validation is conducted on CNN/Daily Mail dataset.ROUGE evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.

A Method of Integrating Length Constraints into Encoder-Decoder Transformer for Abstractive Text Summarization

Text summarization aims to generate a concise version of the original text.The longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended use.Therefore,the problem of generating summary texts with desired lengths is a vital task to put the research into practice.To solve this problem,in this paper,we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization problem.This length parameter is integrated into the encoding phase at each self-attention step and the decoding process by preserving the remaining length for calculating headattention in the generation process and using it as length embeddings added to theword embeddings.We conducted experiments for the proposed model on the two data sets,Cable News Network(CNN)Daily and NEWSROOM,with different desired output lengths.The obtained results show the proposed model’s effectiveness compared with related studies.

Exploring Nominalization in English Abstracts of Applied Linguistics Research Articles:A Comparative Study Between Chinese and International Journals

Researchers around the world strive to communicate new knowledge,primarily via publication,with the abstract being crucial in conveying core insights.Previous research has generally analyzed the discourse features of abstracts from a macro perspective and often employed either outdated texts,such as those over a decade old,or papers written by authors with lower English academic writing proficiency as research material.In this study,we analyzed forty abstracts from leading journals in applied linguistics,evenly split between Chinese and international journals.It revealed that the use of nominalization in abstracts by Chinese and international scholars showed similarities due to the universal academic requirement for conciseness.However,due to cultural and educational differences,each group differed in their respective language choices and nominalization usage.By analyzing the application of nominalization in different cultural contexts,the results of our study offered practical suggestions for crafting abstracts that effectively convey information,thereby,contributing to the broader academic community.

Embodied Meaning in Comprehending Abstract Chinese Counterfactuals

Embodied cognition theories propose that language comprehension triggers a sensorimotor system in the brain.However,most previous research has paid much attention to concrete and factual sentences,and little emphasis has been put on the research of abstract and counterfactual sentences.The primary challenges for embodied theories lie in elucidating the meanings of abstract and counterfactual sentences.The most prevalent explanation is that abstract and counterfactual sentences are grounded in the activation of a sensorimotor system,in exactly the same way as concrete and factual ones.The present research employed a dual-task experimental paradigm to investigate whether the embodied meaning is activated in comprehending action-related abstract Chinese counterfactual sentences through the presence or absence of action-sentence compatibility effect(ACE).Participants were instructed to read and listen to the action-related abstract Chinese factual or counterfactual sentences describing an abstract transfer word towards or away from them,and then move their fingers towards or away from them to press the buttons in the same direction as the motion cue of the transfer verb.The action-sentence compatibility effect was observed in both abstract factual and counterfactual sentences,in line with the embodied cognition theories,which indicated that the embodied meanings were activated in both action-related abstract factuals and counterfactuals.

Intelligent Transformation: General Intelligence Theory

This paper aims to formalize a general definition of intelligence beyond human intelligence. We accomplish this by re-imagining the concept of equality as a fundamental abstraction for relation. We discover that the concept of equality = limits the sensitivity of our mathematics to abstract relationships. We propose a new relation principle that does not rely on the concept of equality but is consistent with existing mathematical abstractions. In essence, this paper proposes a conceptual framework for general interaction and argues that this framework is also an abstraction that satisfies the definition of Intelligence. Hence, we define intelligence as a formalization of generality, represented by the abstraction ∆∞Ο, where each symbol represents the concepts infinitesimal, infinite, and finite respectively. In essence, this paper proposes a General Language Model (GLM), where the abstraction ∆∞Ο represents the foundational relationship of the model. This relation is colloquially termed “The theory of everything”.

欢迎订阅《食品工业》

《食品工业》杂志创刊于1979年,是一份专门从事研究食品科技的综合性科技期刊。是《中国学术期刊网络出版总库》和美国《CHEMICAL ABSTRACTS》收录期刊。《食品工业》在国内外公开发行,邮发代号:4-503,全国各地邮局均可订阅。国内统一连续出版物号:CN 31-1532/TS。

Exploiting comments information to improve legal public opinion news abstractive summarization

Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.

Enhancing N-Gram Based Metrics with Semantics for Better Evaluation of Abstractive Text Summarization

Text summarization is an important task in natural language processing and it has been applied in many applications.Recently,abstractive summarization has attracted many attentions.However,the traditional evaluation metrics that consider little semantic information,are unsuitable for evaluating the quality of deep learning based abstractive summarization models,since these models may generate new words that do not exist in the original text.Moreover,the out-of-vocabulary(OOV)problem that affects the evaluation results,has not been well solved yet.To address these issues,we propose a novel model called ENMS,to enhance existing N-gram based evaluation metrics with semantics.To be specific,we present two types of methods:N-gram based Semantic Matching(NSM for short),and N-gram based Semantic Similarity(NSS for short),to improve several widely-used evaluation metrics including ROUGE(Recall-Oriented Understudy for Gisting Evaluation),BLEU(Bilingual Evaluation Understudy),etc.NSM and NSS work in different ways.The former calculates the matching degree directly,while the latter mainly improves the similarity measurement.Moreover we propose an N-gram representation mechanism to explore the vector representation of N-grams(including skip-grams).It serves as the basis of our ENMS model,in which we exploit some simple but effective integration methods to solve the OOV problem efficiently.Experimental results over the TAC AESOP dataset show that the metrics improved by our methods are well correlated with human judgements and can be used to better evaluate abstractive summarization methods.

Learning of Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning,and discusses several issues related to concept learning,including the assistance of machine learning about concept learning.In terms of individual learning,current evidence shows that the brain tends to process concrete concepts through typical features(shared features);and for abstract concepts,semantic processing is the most important cognitive way.In terms of social learning,interpersonal neural synchrony(INS)is considered the main indicator of efficient knowledge transfer(such as teaching activities between teachers and students),but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning.Ultimately,this article argues that the way the human brain processes concepts depends on the concept’s own characteristics,so there are no“better”strategies in teaching,only more“suitable”strategies.

A Comparative Analysis of Syntactic Complexity in Applied Linguistics Abstracts Written by Chinese Novice Writers and Native English Advanced Writers

The rhetorical structure of abstracts has been a widely discussed topic, as it can greatly enhance the abstract writing skills of second-language writers. This study aims to provide guidance on the syntactic features that L2 learners can employ, as well as suggest which features they should focus on in English academic writing. To achieve this, all samples were analyzed for rhetorical moves using Hyland’s five-rhetorical move model. Additionally, all sentences were evaluated for syntactic complexity, considering measures such as global, clausal and phrasal complexity. The findings reveal that expert writers exhibit a more balanced use of syntactic complexity across moves, effectively fulfilling the rhetorical objectives of abstracts. On the other hand, MA students tend to rely excessively on embedded structures and dependent clauses in an attempt to increase complexity. The implications of these findings for academic writing research, pedagogy, and assessment are thoroughly discussed.

Ext-ICAS:A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

With the continuous growth of online news articles,there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading.Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline.Abstractive summarization task is framed as seq2seq modeling.Existing seq2seq methods perform better on short sequences;however,for long sequences,the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper.The novelty is to parallelize the sequence computation training by incorporating feed-forward,the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity(Ext-ICAS)with sentence dependency position.Also,it does not require any normalization technique explicitly.Our proposed abstractive Bidirectional Long Short Term Memory(Bi-LSTM)encoder sequence model performs better than the Bidirectional Gated Recurrent Unit(Bi-GRU)encoder with minimum training loss and with fast convergence.The proposed model was evaluated on the Cable News Network(CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59%with an average number of similarity computations.

Intelligent acupuncture:data-driven revolution of traditional Chinese medicine

Acupuncture,a form of traditional Chinese medicine with a history of 2,000 years in China,has gained wider acceptance worldwide as a complementary therapy.Studies have examined its effectiveness in various health conditions and it is commonly used alongside conventional medical treatments.With the development of artificial intelligence(AI)technology,new possibilities for improving the efficacy and precision of acupuncture have emerged.This study explored the combination of traditional acupuncture and AI technology from three perspectives:acupuncture diagnosis,prescription,and treatment evaluation.The study aimed to provide cutting-edge direction and theoretical assistance for the development of an acupuncture robot.

Transactions of Nanjing University of Aeronautics and Astronautics Information for Contributors

Transactions of Nanjing University of Aeronautics&Astronautics(TNUAA)is a bimonthly journal facing international academic community.Emphasizing prompt and effective dissemination of key data and new scientific insights,TNUAA publishes experimental and theoretical papers bearing on applications to all branches of aeronautics,astronautics and civil aviation.TNUAA is currently indexed by Engineering Index(Ei Compendex,USA),Scopus(Sco,Holland),Chemical Abstracts(CA,USA),P.Ж.(Russia),EBSCO(USA),SA(England),Zbl(Germany),Cambridge Scientific Abstracts(CSA),Chinese Science Citation Database(CSCD,China),National Knowledge Infrastructure(CNKI,China)and Chinese S&T Journal Citation Reports(China).

Bug localization based on syntactical and semantic information of source code

The existing software bug localization models treat the source file as natural language, which leads to the loss of syntactical and structure information of the source file. A bug localization model based on syntactical and semantic information of source code is proposed. Firstly, abstract syntax tree(AST) is divided based on node category to obtain statement sequence. The statement tree is encoded into vectors to capture lexical and syntactical knowledge at the statement level.Secondly, the source code is transformed into vector representation by the sequence naturalness of the statement. Therefore,the problem of gradient vanishing and explosion caused by a large AST size is obviated when using AST to the represent source code. Finally, the correlation between bug reports and source files are comprehensively analyzed from three aspects of syntax, semantics and text to locate the buggy code. Experiments show that compared with other standard models, the proposed model improves the performance of bug localization, and it has good advantages in mean reciprocal rank(MRR), mean average precision(MAP) and Top N Rank.

A comprehensive review of azadirachtin: physicochemical properties, bioactivities, production, and biosynthesis

Azadirachtin,a complex tetratriterpenoid limonin with potent insecticidal properties,is the most widely used biological pesticide worldwide.Its versatile pharmacological applications include the inhibition of tumor growth and anti-malarial,anti-bacterial,and anti-inflammatory properties.Azadirachtin plays a pivotal role in pest control and novel drug development.The primary source of azadirachtin is the neem tree(Azadirachta indica A.Juss),with an azadirachtin content ranging from 0.3%to 0.5%.Despite the market demand for botanical pesticides reaching approximately 100,000 tons per year,the annual neem production in China is only 1.14 tons.Although azadirachtin can be obtained through plant extraction or chemical synthesis,the quantity obtained does not meet the market demand in China.The sluggish pace of azadirachtin biosynthesis results from the limited availability of genetic information and the complexity of the synthetic pathway.Recent advancements in azadirachtin biosynthesis hold promise as an efficient collection method.In this study,we explored the physicochemical properties,biological activities,mechanisms of action,and acquisition methods of azadirachtin.We also delved into recent progress in azadirachtin biosynthesis and assessed potential future usage challenges.This study aims to establish a theoretical foundation for the scientific application and efficient synthesis of azadirachtin,offering valuable reference information to the industry.

Abstraction and Representation:Form and Meaning in the Philosophy of Images

This article examines the complex interplay between abstraction and representation in the ontology of images.Images inhabit an in-between space as tangible artifacts that also convey intangible ideas and meanings.The analysis synthesizes perspectives from across the history of philosophy to elucidate how images bridge abstraction and representation through their form and function.It engages with ongoing epistemological and aesthetic debates concerning the dual nature of images.Plato’s theory of ideal forms is outlined as an early attempt to define abstraction.Modern semiotic theories are discussed for their insights into how images create meaning through codes and signs.Phenomenology offers an alternative approach by prioritizing the sensorial,affective impact of images.Poststructuralism problematizes representation in the context of mechanical reproduction and simulacra.While diverse,these philosophical frameworks all grapple with the issues images pose between abstract essence and concrete appearance,conceptual ideas and sensory manifestations.The article reveals the richness of images as liminal constructs that collapse dualisms in their creative interfacing of material forms and immaterial meanings.It concludes that this ontological ambiguity empowers images as mediators between imagination and perception,subjectivity and reality.

欢迎订阅《食品工业》

《食品工业》杂志创刊于1979年,是一份专门从事研究食品科技的综合性科技期刊。是《中国学术期刊网络出版总库》和美国《CHEMICAL ABSTRACTS》收录期刊。《食品工业》在国内外公开发行,邮发代号:4-503,全国各地邮局均可订阅。国内统一连续出版物号:CN31-1532/TS。

欢迎订阅《食品工业》

《食品工业》杂志创刊于1979年,是一份专门从事研究食品科技的综合性科技期刊。是《中国学术期刊网络出版总库》和美国《CHEMICAL ABSTRACTS》收录期刊。《食品工业》在国内外公开发行,邮发代号:4-503,全国各地邮局均可订阅。国内统一连续出版物号:CN 31-1532/TS。

欢迎订阅《食品工业》

《食品工业》杂志创刊于1979年,是一份专门从事研究食品科技的综合性科技期刊。是《中国学术期刊网络出版总库》和美国《CHEMICAL ABSTRACTS》收录期刊。《食品工业》在国内外公开发行,邮发代号:4-503,全国各地邮局均可订阅。国内统一连续出版物号:CN31-1532/TS。