Ensemble Summarization Models to Leverage Performance
Afamefuna Promise Umejiaku, Fei Zhou, Victor S. Sheng · 2022
Summaries underpin a majority of relevant information needed to quickly make an informed decision from a large corpus of text; Natural Language techniques have been developed to generate these summaries using either abstractive or extractive methods. Presently, state-of-the-art approaches involve using neural network-based solutions akin to seq2seq, graph2seq, and other encoder-decoder architectures. These models make different contributions to prediction quality. In this paper, we build a model that ensembles two distinct pretraining NLP models to leverage their summarization performance using a TextRank process we constructed. We evaluate our model using the CoronaNet Research Project COVID-19 dataset, which contains how governments responded to the Covid-19 pandemic. We compared the ROUGE scores of the individual models on the test set to our ensemble method. The experiment results show that our proposed ensemble method performs better than using the models individually.