Neural Abstractive Unsupervised Summarization of Online News Discussions
This work addresses the problem of summarizing social media discussions for users and platforms, though it is incremental as it builds on BERT-based architectures.
The paper tackles the challenge of summarizing multi-author online news discussions by introducing a novel abstractive method that incorporates social context through comment likes, achieving significantly higher ROUGE scores compared to existing extractive and abstractive baselines.
Summarization has usually relied on gold standard summaries to train extractive or abstractive models. Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. We address this challenging task by introducing a novel method that generates abstractive summaries of online news discussions. Our method extends a BERT-based architecture, including an attention encoding that fed comments' likes during the training stage. To train our model, we define a task which consists of reconstructing high impact comments based on popularity (likes). Accordingly, our model learns to summarize online discussions based on their most relevant comments. Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online social networks. Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread. Our model, including the social attention encoding, significantly outperforms both extractive and abstractive summarization methods based on such evaluation.