  Recently, encoder-decoder models are widely used in social media text
summarization. However, these models sometimes select noise words in irrelevant
sentences as part of a summary by error, thus declining the performance. In
order to inhibit irrelevant sentences and focus on key information, we propose
an effective approach by learning sentence weight distribution. In our model,
we build a multi-layer perceptron to predict sentence weights. During training,
we use the ROUGE score as an alternative to the estimated sentence weight, and
try to minimize the gap between estimated weights and predicted weights. In
this way, we encourage our model to focus on the key sentences, which have high
relevance with the summary. Experimental results show that our approach
outperforms baselines on a large-scale social media corpus.
