[Paper Review] Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization

Chenguang Zhu, Ziyi Yang+3 more|arXiv (Cornell University)|Sep 25, 2019

Topic Modeling15 citations

TL;DR

This paper proposes leveraging lead bias in news articles as a pretraining objective for abstractive summarization: training a transformer model to predict the lead sentences from the remainder of the article. Through careful data filtering, the model achieves strong zero-shot performance and outperforms strong baselines after fine-tuning, with human evaluations confirming its effectiveness.

ABSTRACT

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 unlabeled 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. Human evaluations further show the effectiveness of our method.

Motivation & Objective

• To address the challenge of weak supervision in abstractive news summarization by exploiting the inherent lead bias in news articles.
• To develop a self-supervised pretraining method that improves model performance without requiring labeled summary data during pretraining.
• To enable strong zero-shot performance on news summarization tasks using only unlabeled news corpora.
• To demonstrate that lead sentence prediction can serve as an effective pretraining objective for abstractive summarization models.
• To validate the method through both automatic and human evaluations on downstream summarization tasks.

Proposed method

• Pretrain a transformer-based model to predict the leading sentences of news articles using only the rest of the article as context.
• Use a large-scale unlabeled news corpus to train the model on the lead-prediction objective, exploiting the natural lead bias in journalistic writing.
• Apply rigorous data cleaning and filtering to ensure high-quality training examples and reduce noise in the pretraining data.
• Train the model end-to-end using standard transformer objectives, with no additional fine-tuning required for zero-shot evaluation.
• Fine-tune the pretrained model on downstream summarization tasks using standard supervised fine-tuning protocols.
• Use the model's attention mechanisms and decoder autoregressive generation to produce abstractive summaries from input articles.

Experimental results

Research questions

• RQ1 Can lead bias in news articles be effectively leveraged as a self-supervised pretraining objective for abstractive summarization?
• RQ2 How does a model pretrained on lead sentence prediction perform in zero-shot settings compared to supervised baselines?
• RQ3 Does fine-tuning the lead-prediction pretrained model yield performance gains on standard news summarization benchmarks?
• RQ4 How does the model's performance compare to strong competitive models in both automatic and human evaluations?

Key findings

• The pretrained model without any fine-tuning achieves strong zero-shot performance on news summarization tasks, demonstrating the effectiveness of the lead-prediction objective.
• After fine-tuning, the model outperforms multiple competitive baseline models on standard news summarization benchmarks.
• Human evaluations confirm that the summaries generated by the model are of high quality and comparable to or better than those from strong baselines.
• The method effectively leverages the natural lead bias in news articles to pretrain models without requiring labeled summaries during pretraining.
• The approach is simple, scalable, and effective, achieving state-of-the-art results with minimal architectural modifications.