Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
This work addresses abstractive summarization for informal text like Reddit posts, offering a new dataset and model, but it is incremental as it builds on existing summarization techniques.
The authors tackled abstractive summarization by introducing the Reddit TIFU dataset of 120K informal posts and a multi-level memory network model, showing that the dataset is highly abstractive and the model outperforms state-of-the-art methods in evaluations.
We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually locate at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.