Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information. While many algorithms exploit this fact in summary generation, it has a detrimental effect on teaching the model to discriminate and extract important information. We propose that the lead bias can be leveraged in a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabelled corpus: predicting the leading sentences using the rest of an article. Via careful data cleaning and filtering, our transformer-based pretrained model without any finetuning achieves remarkable results over various news summarization tasks. With further finetuning, our model outperforms many competitive baseline models. For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset. The finetuned model obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset.
This paper suggests generating a large news summarization dataset by taking advantage of the fact that in news articles it is often the case that first few sentences contain the most important information. I have the following criticisms of this paper: - the idea is not novel. The XSUM dataset cited had used this to create a large dataset based on BBC articles as the editorial guidelines are such that the first sentence is a summary of the article. The lead1 baseline doesn*t make sense, as it is the actual reference of the dataset. As implemented, it actually picks the second sentence of the original article, and unsurprisingly works worse than the lead-X for the other two datasets. - the filtering based on word overlap between the initial sentences and the rest of the document means that the training dataset will encourage models copying words; good summaries don*t have high word overlap necessarily. - no human evaluation is not conducted; ROUGE indicates small differences, but it can*t be trusted without confirmation by human evaluation - I don*t agree that using positional information is bad for the models. The point is that we need to do better than that, but we should still take it into account
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
Lead bias is a common phenomenon in news summarization, where early parts of an article often contain the most salient information. While many algorithms exploit this fact in summary generation, it has a detrimental effect on teaching the model to discriminate and extract important information. We propose that the lead bias can be leveraged in a simple and effective way in our favor to pretrain abstractive news summarization models on large-scale unlabelled corpus: predicting the leading sentences using the rest of an article. Via careful data cleaning and filtering, our transformer-based pretrained model without any finetuning achieves remarkable results over various news summarization tasks. With further finetuning, our model outperforms many competitive baseline models. For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset. The finetuned model obtains 3.2% higher ROUGE-1, 1.6% higher ROUGE-2 and 2.1% higher ROUGE-L scores than the best baseline model on XSum dataset.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper suggests generating a large news summarization dataset by taking advantage of the fact that in news articles it is often the case that first few sentences contain the most important information. I have the following criticisms of this paper: - the idea is not novel. The XSUM dataset cited had used this to create a large dataset based on BBC articles as the editorial guidelines are such that the first sentence is a summary of the article. The lead1 baseline doesn*t make sense, as it is the actual reference of the dataset. As implemented, it actually picks the second sentence of the original article, and unsurprisingly works worse than the lead-X for the other two datasets. - the filtering based on word overlap between the initial sentences and the rest of the document means that the training dataset will encourage models copying words; good summaries don*t have high word overlap necessarily. - no human evaluation is not conducted; ROUGE indicates small differences, but it can*t be trusted without confirmation by human evaluation - I don*t agree that using positional information is bad for the models. The point is that we need to do better than that, but we should still take it into account
assistant
This paper suggests generating a large news summarization dataset by taking advantage of the fact that in news articles it is often the case that first few sentences contain the most important information. I have the following criticisms of this paper: - the idea is not novel. The XSUM dataset cited had used this to create a large dataset based on BBC articles as the editorial guidelines are such that the first sentence is a summary of the article. The lead1 baseline doesn*t make sense, as it is the actual reference of the dataset. As implemented, it actually picks the second sentence of the original article, and unsurprisingly works worse than the lead-X for the other two datasets. - the filtering based on word overlap between the initial sentences and the rest of the document means that the training dataset will encourage models copying words; good summaries don*t have high word overlap necessarily. - no human evaluation is not conducted; ROUGE indicates small differences, but it can*t be trusted without confirmation by human evaluation - I don*t agree that using positional information is bad for the models. The point is that we need to do better than that, but we should still take it into account