Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

TL;DR

This paper introduces a new Reddit dataset for abstractive summarization and proposes a multi-level memory network model that outperforms existing models, leveraging informal online posts for more unbiased summaries.

Contribution

The paper presents a novel Reddit TIFU dataset and a multi-level memory network model for abstractive summarization, addressing biases in formal datasets and improving summarization quality.

Findings

Reddit TIFU dataset is highly abstractive.

MMN outperforms state-of-the-art models.

User studies confirm model effectiveness.

Abstract

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.