Neural- DINF : A Neural Network based Framework for Measuring Document Influence (2020)

Abstract: Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM.

## introduction identifying the most influential articles is of great importance in many areas of research. it is often the case that we are increasingly exposed to numerous papers published every day. research on influence evaluation can be applied to measure the scholarly impact of universities and research facilities. besides, it helps researchers to distinguish valuable research work from a large number of scientific papers. the common approach of assessing an article's research impact is to count the number of explicit references to it. however, citations are often not available. for example, collections including blog posts and government documents adopt ideas proposed in the documents without explicit references @xcite . to identify influential articles without citations, @xcite and @xcite proposed probabilistic methods, which are based on dynamic topic models @xcite . they aimed to identify influential articles by examining the word frequency change over time. in this paper, we aim to use both word frequency changes and word semantic shifts on measuring document influence without citations. for our purpose, we propose a neural network based method called neural-dinf, which stands for a neural network based framework for measuring document influence. our idea is that words that have semantic shifts across time contribute significantly to the influence of a document. recent studies show that words whose word embeddings across different time periods diverge significantly are suspected to have semantic shifts @xcite @xcite . neural-dinf first generates static word embeddings in each time period by using word2vec @xcite independently, then aligns embeddings to the same vector space with an unsupervised method, subsequently calculates differences of the embeddings of many words across time to identify words that experience semantic shifts, finally measures the influence of a document by counting these crucial words. in summary, this paper makes the following main contributions: this paper is organized as follows: section 2 states related work; section 3 formulates our approach; section 4 presents our experiments; section 5 concludes our work. ## related work there are two lines of literature that are closely related to our work: document influence evaluation and semantic shift detection. ## method our neural-dinf contains the following three steps. first, we generate static word embeddings in each time slice separately. then, we implement an unsupervised approach with adversarial training and a refinement procedure to align these embeddings to the same vector space. finally, we present a new metric to evaluate the influence of a document without citations. ## conclusion in this paper, we aim to evaluate document influence from a fine-grained level by additionally considering word semantic shifts. for our purpose, we develop neural-dinf which measures document influence from the texts of documents. besides, we propose an unsupervised method to address the alignment problem. the document receives an influence score based on how it explains the word frequency change and the word semantic shift. our experimental results show that our model performs better than the dim on acl anthology.