Keyphrase Generation via Soft and Hard Semantic Corrections (2022)

Abstract: Keyphrase generation aims to generate a set of condensed phrases given a source document. Although maximum likelihood estimation (MLE) based keyphrase generation methods have shown impressive performance, they suffer from the bias on the source-prediction sequence pair and the bias on the prediction-target pair. To tackle the above biases, we propose a novel correction model CorrKG on top of the MLE pipeline, where the biases are corrected via the optimal transport (OT) and a frequency-based filtering-and-sorting (FreqFS) strategy. Specifically, OT is introduced as soft correction to facilitate the alignment of salient information and rectify the semantic bias in the source document and predicted keyphrases pair. An adaptive semantic mass learning scheme is conducted on the vanilla OT to achieve a proper pair-wise optimal transport procedure, which promotes the OT learning brought by rectifying semantic masses dynamically. Besides, the FreqFS strategy is designed as hard correction to reduce the bias of predicted and ground truth keyphrases, and thus to generate accurate and sufficient keyphrases. Extensive experiments over multiple benchmark datasets show that our model achieves superior keyphrase generation as compared with the state-of-the-arts.

## methodology the proposed corrkg consists of three components: (1) a bart backbone; figure 2 shows the architecture. we details these components in the following subsections. ## conclusion towards the semantic biases that existed in keyphrase generation, this paper presents a softhard correction mechanism oriented model cor-rkg. the novelty in our model is twofold. first, optimal transport theory is introduced to correct the semantic bias between the source document and predictions. furthermore, an adaptive mass learning scheme is designed to better fit ot. second, the freqfs strategy is proposed to exploit the consistencies between the predicted and target keyphrases. extensive experiments show that corrkg is capable of generating high-quality and diverse keyphrases.