Paper:
We present a new approach to defining a sequence loss function to train a summarizer by using a secondary encoder-decoder as a loss function, alleviating a shortcoming of word level training for sequence outputs. The technique is based on the intuition that if a summary is a good one, it should contain the most essential information from the original article, and therefore should itself be a good input sequence, in lieu of the original, from which a summary can be generated. We present experimental results where we apply this additional loss function to a general abstractive summarizer on a news summarization dataset. The result is an improvement in the ROUGE metric and an especially large improvement in human evaluations, suggesting enhanced performance that is competitive with specialized state-of-the-art models.

Review:
This paper proposes to use an additional component to the commonly used encoder-decoder approach for summarization, which is referred to as the recoder, which is an RNN-syle component that takes the output of the decoder. The intuition offered in the paper is that a good summary should produce itself via the recoder network, and in training it together with the original encoder-decoder it should improve its performance as it would be able to capture more than what the word-level loss does.
I have the following objections to this paper:
- I can't see why this extra component should improve the quality of the summary produced. If say our encoder-decoder architecture models the training data perfectly, then a recoder that does not do anything would be the right choice. Taking this further, a recoder could actually be fixing problems in the output of the decoder, and thus not providing a good training signal. It doesn't make sense to my that a summary should produce itself via a neural network, unless we are training an auto-encoder. The experiments validate this; the difference in ROUGE score are less than a point, which is the kind of fluctuation one expects due to random seed differences, etc.
- I find it odd that ROUGE score is dismissed as a loss to train against (referred to as a heuristic in the end of section 4.1), but then 1 ROUGE point difference is considered a "significant" improvement (making such claims without statistical significance testing is misleading). Sure it has flaws, if there is something better why not use it for evaluation? Furthermore, claiming that the recoder does a better job requires evidence. Why not train this extra function against human judgements? Assuming that what is wanted is to train a model that estimates the quality of the summary, it would make sense to look at the approaches used for the similar task of machine translation quality estimation: https://www.mitpressjournals.org/doi/pdf/10.1162/tacl_a_00056
- The approach is in my view a kind of actor-critic approach; the recoder could play that role, and in fact the Bahdanau et al. 2016 paper cited does this. However no comparison is offered, be it theoretical or experimental. Furthermore, the criticism that sequence-level training requires differentiable losses is incorrect; MIXER for example does train against scores such as ROUGE that are not differentiable. Furthermore, the BSO approach by Wiseman and Rush (2016) cited does give a continuous output to optimize for seq2seq that is asked for in the beginning of page 4.
- In the experiments, how was the length penalty determined to be the graduated curve mentioned in section 5? I would expect to have comparison against other approaches that try to train encoder-decoder to improve summarization, such as those mentioned in section 4.1.
- On the human evaluation experiments: the difference between the two models is quite small, especially given that the workers were not allowed to say that the models were equally good/bad. Furthermore, there is no inter-annotator agreement, as each comparison was done by a single crowd worker. Showing only the first 400 tokens of the original document would incorrectly disadvantage models selecting content from later in the articles. Finally, showing the reference summary creates another bias, since equally good summaries can disagree on what content to include. It might be helpful to look at some recent work on manual evaluation of summarization that tried to address these issues: https://arxiv.org/abs/1906.01361