人工智慧自動文本摘要研究

A Study of Artificial Intelligence for Automatic Text Summarization

陳昭妤(Chao-Yu Chen)

指導教授 ： 戴敏育

淡江大學/商管學院/資訊管理學系碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00999

摘要

隨著時代改變與技術發展，人們接收到的資訊量大增，協助人們快速抓取到關鍵變成是一大重點。自動文本摘要便是其中一大議題，依據文本的類型、摘要生成的方式、目標摘要的形式以及軟硬體技術的支援程度不同來決定使用的理論與技術。過去的文獻中較少以深度學習技術來解決產生標題（短摘要）的問題，因此本研究欲檢驗深度學習應用於短摘要生成的效果。
本研究使用WOS資料庫來收集49724筆情感分析相關的論文資料，使用前處理後的論文摘要與標題來訓練兩種不同方法建置的模組，並以ROUGE評估與標準標題的相似度。
經由本研究兩大模組的比較，可發現傳統統計性模組於ROUGE-1及ROUGE-L的表現較佳，而深度學習模組在ROUGE-2以及各種評估機制的精確度較佳。

關鍵字

人工智慧 ； 序列到序列 ； 自動文本摘要 ； 深度學習 ； 遞迴類神經網路

並列摘要

Automatic text summarization has played a critical role in helping people obtain key information from increasing huge data with the advantaged development of technology. In the past, few literatures are related to solve the problem of generating titles (short summaries) by using artificial intelligence (AI). The purpose of this study is that we proposed an AI approach for automatic text summarization. We developed an AI text summarization system architecture with two models, namely, statistical model, and deep learning model as well as evaluating the performance of two models. Essay titles and essay abstracts are used to train artificial intelligence deep learning model to generate the candidate titles and evaluated by ROUGE for performance evaluation. We used 5-fold cross evaluation to evaluate the performance. In ROUGE-1 and ROUGE-L, the performance of statistical model is better. In ROGUGE-2, the performance of deep learning model is better. The contribution of this paper is that we proposed an AI automatic text summarization system by applying deep learning to generate short summaries from the titles and abstracts of the Web of Science (WOS) database.

並列關鍵字

Artificial Intelligence ； Sequence-to-Sequence ； Automatic Text Summarization ； Deep Learning ； Recurrent Neural Networks

參考文獻

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539

林大貴. (2017). TensorFlow+Keras深度學習人工智慧實務應用 (1 ed.): 博碩.

洪敬政. (2017). 情感分析研究的知識結構. 淡江大學,

Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

Baxendale, P. B. (1958). Machine-made index for technical literature—an experiment. IBM Journal of Research and Development, 2(4), 354-361.