Extractive Summarization with Text Generator (2024)

Abstract: Standard extractive systems suffer from the lack of gold training signals since existing corpora solely provide document and human-written summary pairs while disregarding extractive labels. As a result, existing methods resort to imperfect pseudo-labels that are both biased and error-prone, thereby hindering the learning process of extractive models. In contrast, text generators which are commonly employed in abstractive summarization can effortlessly overcome this predicament on account of flexible sequence-to-sequence architectures. Motivated to bypass this inherent limitation, we investigate the possibility of conducting extractive summarization with text generators. Through extensive experiments covering six summarization benchmarks, we show that high-quality extractive summaries can be assembled via approximating the outputs (abstractive summaries) of these generators. Moreover, we find that the approximate summaries correlate positively with the auxiliary summaries (i.e. a better generator enables the production of better extractive summaries). Our results signify a new paradigm for training extractive summarizers i.e. learning with generation (abstractive) objectives rather than extractive schemes.

## conclusion in this work, we explore the use of existing abstractive models for extractive summarization. we make no assumption on the underlying abstractive models and follow a black-box approach. utilising abstractive summaries, we show that state-of-theart extractive summaries can be achieved without extractive training. to validate the method's effectiveness, we conduct extensive experiments on six datasets and provide comparison with existing methods, where our models demonstrate either superior or comparable performance.