Generating fluent, coherent, and informative text from structured data is called table-to-text generation. Copying words from the table is a common method to solve the “out-of-vocabulary” problem, but it’s difficult to achieve accurate copying. In order to overcome this problem, we invent an auto-regressive framework based on the transformer that combines a copying mechanism and language modeling to generate target texts. Firstly, to make the model better learn the semantic relevance between table and text, we apply a word transformation method, which incorporates the field and position information into the target text to acquire the position of where to copy. Then we propose two auxiliary learning objectives, namely table-text constraint loss and copy loss. Table-text constraint loss is used to effectively model table inputs, whereas copy loss is exploited to precisely copy word fragments from a table. Furthermore, we improve the text search strategy to reduce the probability of generating incoherent and repetitive sentences. The model is verified by experiments on two datasets and better results are obtained than the baseline model. On WIKIBIO, the result is improved from 45.47 to 46.87 on BLEU and from 41.54 to 42.28 on ROUGE. On ROTOWIRE, the result is increased by 4.29% on CO metric, and 1.93 points higher on BLEU.
Table-to-text generation is an important task in natural language generation that aims to generate smooth, informative text based on structured data. In this paper, we propose a novel transformer-based autoregressive model that incorporates table content copying and language model based generation. At first, we propose a word transformation method to process a target text. By using target text containing fields and position information, we can help the model learn the relationship between target text and table and gain the position of where to copy. We then propose two auxiliary learning goals: table-text constraint loss and copy loss. Table-text constraint loss is introduced to effectively model table inputs, whereas copy loss is exploited to precisely copy word fragments from a table. In addition, we change the maximization-based text search strategy to reduce the probability of problems such as sentence repetition and inconsistency. On the WIKIBIO dataset, our model improves its BLUE scores from 45.47 to 46.87 and ROUGE scores from 41.54 to 42.28, outperforming state-of-the-art baseline models on automatic evaluation metrics. On the ROTOWIRE test set, compared with the best baseline model, our model gets 4.29% higher on CO metric, and 1.93 points higher on BLEU.
Information push has become a key technology in the age of new media, which is especially important in organizations of news gathering and pressing. This paper first introduced the cloud push technology, and then analyzed the demand for comprehensive information push in current news media, stated the significance of the push system. The push system was designed and implemented finally.
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