Neural Abstractive Unsupervised Summarization of Online News Discussions
- URL: http://arxiv.org/abs/2106.03953v1
- Date: Mon, 7 Jun 2021 20:33:51 GMT
- Title: Neural Abstractive Unsupervised Summarization of Online News Discussions
- Authors: Ignacio Tampe Palma, Marcelo Mendoza, and Evangelos Milios
- Abstract summary: We introduce a novel method that generates abstractive summaries of online news discussions.
Our model is evaluated using ROUGE scores between the generated summary and each comment on the thread.
- Score: 1.2617078020344619
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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