Abstract This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of triples.Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder's oblivion of the semantics of the answer entity.We propose an innovative knowledgeenriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges.In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG.Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types.Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning.Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.

Conventional models on Neural Text Generation (NTG) determine the output distribution by applying maximum likelihood estimation on training corpora. However, as user preference for generated content can be constantly changing, an optimized text generator needs to assimilate such non-static nature into the outcome adaptively. In this paper, our goal is to generate product descriptions on e-commerce platforms, and we explore this classic task from a novel perspective that allows the optimal output text to vary with ever-changing user preference. Specifically, we propose an evolutionary NTG model to enable its interactive environment to fine-tune the pre-trained generative policy via Reinforcement Learning (RL). To this end, a dynamic context of textual fitness is established based on the user click behavior associated with previously generated content to estimate reward/penalty signals for each output text. Our motivation is to leverage the click-through rate as a kind of user-centric measurement on text quality, by which we can assess how likely a product description attracts people's attention and follows shopping trends. Extensive experiments on a real e-commerce website demonstrate that the proposed approach achieves a significant superiority over two statically RL-based variants and four state-of-the-art NTG solutions.

Social advertisement has emerged as a viable means to improve purchase sharing in the context of e-commerce. However, humanly generating lots of advertising scripts can be prohibitive to both e-platforms and online sellers, and moreover, developing the desired auto-generator will need substantial gold-standard training samples. In this paper, we put forward a novel seq2seq model to generate social advertisements automatically, in which a quality-sensitive loss function is proposed based on user click behavior to differentiate training samples of varied qualities. Our motivation is to leverage the clickthrough data as a kind of quality indicator to measure the textual fitness of each training sample quantitatively, and only those ground truths that satisfy social media users will be considered the eligible and able to optimize the social advertisement generation. Specifically, under the qualified case, the ground truth should be utilized to supervise the whole training phase as much as possible, whereas in the opposite situation, the generated result ought to preserve the semantics of original input to the greatest extent. Simulation experiments on a large-scale dataset demonstrate that our approach achieves a significant superiority over two existing methods of distant supervision and three state-of-the-art NLG solutions.

Conventional solutions to automatic related work summarization rely heavily on humanengineered features.In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously.Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately.Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.