From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Accept

From: NIPS.cc/2018/Workshop/IRASL/Paper5/AnonReviewer2
title: An interesting contribution to neural abstractive summarization
review: This study proposes a multi-task learning approach using semantic role recognition in addition to summarization through two different kinds of decoders. This approach needs to be able to get a textual corpus (automatically or manually) annotated with semantic role at training time (no necessary at inference time).
This studies also introduces the use of multi-head attention mechanism for the summarization task, and different propositions about reranking (beam search).

Experiments are done on accessible data and shows a real interest about the proposed approach, particularly to deal with adversarial content (noise) in texts to process. Performance are evaluated through objective and subjective metrics.

I appreciated a lot this paper. Even if I know that size (number of pages) constraint is hard to respect with a so rich paper, if this paper is accepted, please increase the size of the figures. Some texts in the figures are hard to read on printed paper.
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper5/AnonReviewer1
title: A lucid look at reducing the effect of adversarial material in abstractive summarization 
review: The manuscript addresses the problem of robustness to adversarial information in abstractive summarization. It introduces a novel semantic-aware model able to interpret salient content, and a novel evaluation scheme systematically incorporating adversarial examples. Human evaluation also confirms that the resulting summaries are uniformly more informative and faithful as well as less redundant than a baseline seq2seq model.

- Pro's:

The paper is well written and the basic premise of learning both salient semantic roles and a summary in a single end-to-end trained neural network is clearly superior to the previous two-step approach of content selection followed by surface realization. In addition, the experimental results are competently set up and reasonably informative to the community. 

- Con's:

I have two reservations:
* the reranking-based summary decoder feels a bit unprincipled — this makes it hard to determine the practical usefulness of the proposed enhancements across different use cases
* it is unclear how much the benefit of extracting semantic roles is tied to the genre of documents considered (in this case, news) — experiments should be repeated across different corpora in order to determine what type of semantic structure bears the most information in each case.

rating: 4: Top 50% of accepted papers, clear accept
confidence: 3: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature