Refining Data for Text Generation

Wenyu Guan, Qianying Liu, Tianyi Li, Sujian Li


Abstract
Recent work on data-to-text generation has made progress under the neural encoder-decoder architectures. However, the data input size is often enormous, while not all data records are important for text generation and inappropriate input may bring noise into the final output. To solve this problem, we propose a two-step approach which first selects and orders the important data records and then generates text from the noise-reduced data. Here we propose a learning to rank model to rank the importance of each record which is supervised by a relation extractor. With the noise-reduced data as input, we implement a text generator which sequentially models the input data records and emits a summary. Experiments on the ROTOWIRE dataset verifies the effectiveness of our proposed method in both performance and efficiency.
Anthology ID:
2020.ccl-1.82
Volume:
Proceedings of the 19th Chinese National Conference on Computational Linguistics
Month:
October
Year:
2020
Address:
Haikou, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
881–891
Language:
English
URL:
https://www.aclweb.org/anthology/2020.ccl-1.82
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ccl-1.82.pdf