Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization (2021)

Abstract: Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process. In this work, we design a heterogeneous semantic slot graph with a slot-level mask cross-attention to enhance the slot features for more correct summarization. We also propose a slot-driven beam search algorithm in the decoding process to give priority to generating salient elements in a limited length by “filling-in-the-blanks”. Besides, an adversarial contrastive learning assisting the training process is introduced to alleviate the exposure bias. Experimental performance on different types of factual errors shows the effectiveness of our methods and human evaluation further verifies the results..

## introduction current state-of-the-art conditional text generation models accomplish a high level of fluency and informativeness, mostly thanks to advances in seq2seq architectures with the attention and copy mechanisms @xcite and the pre-trained transformer-based models for natural language understanding @xcite . despite this progress @xcite , there are still many limitations facing neural text summarization, the most serious of which is their tendency to generate summaries with a substantial number of factual errors. besides, the rouge scores @xcite , the most commonly used evaluation metrics, are inadequate to quantify factual correctness and only capture the information coverage at token-level, i.e., n-gram overlap, which does not always convey the desired semantics and reach consensus with human judgement. recently, as people increasingly exchange information in the way of dialogue, giving a highquality summarization for the dialogue is particularly necessary, which can help people quickly grasp the core information of the long dialogue history without reviewing the complex context and is significant to improve the efficiency of social contact. however, as a special kind of text form, the dialogue is usually informal and dynamic. utterances are often said by different speakers alternately in different language styles, which leads to the description of one event being fragmented and scattered in multiple utterances. these inherent differences between dialogues and documents make it easier to product various factual errors, i.e., factual inconsistent and incomplete, in the generated summaries, as shown in fig. 1 . therefore, it is urgent to develop a neural model, focusing on exploring the factual correctness, to generate overall high-quality summaries for the multi-people dialogue scene. there have been some recent researches on abstractive dialogue summarization, such as deploying document summarization methods to the conversation settings @xcite , utilizing the dialogue acts @xcite and key point sequences @xcite , topic word information @xcite , and analyzing the conversational structures @xcite . other researches have also pushed the frontier of guaranteeing the factual consistency in abstractive document summarization systems via proposing related evaluation metrics @xcite and designing models @xcite . however, the current methods (1) fail to utilize the unique semantic and structural information of dialogues to identify the salient elements and guide the decoding process, so that to deal with factual errors for dialogue summarization, (2) lack overall evaluation metrics for factual correctness. we argue that the slot-aware structure is important to improve the performance of factual correctness for dialogue summarization. besides, except for factual consistency metric, the factual completeness metric is also an indispensable key to evaluate factual correctness. in this paper, we propose a semantic slot guided adversarial sequence-to-sequence network (ssanet). the ssanet contains a heterogeneous semantic slot (hss) graph, where different types of nodes represent different slot labels and the edges are the dependencies between slot values. attentions of three different granularities, i.e. tokens, utterances, and slots, are unified into one architecture to promote the learning of the relationships between all granularities. crucially, the slot-level attention mechanism can make the model directly select the appropriate slot features from the hss graph to fill the corresponding slot in the summary sequence, which ensures the correctness and completeness of the salient information in the generated content. in the decoding process, we propose a slotdriven beam search algorithm based on @xcite to give priority to generating salient elements in a limited length by "filling-in-the-blanks". besides, to alleviate the exposure bias, we also use a contrastive learning strategy with adversarial perturbations @xcite ) by actively exposing some wrong tokens during training. finally, we propose a new evaluation metric to quantify factual completeness at the slot-level. our contributions can be summarized as follows: ## methodology as illustrated in fig. 2 , our methods include three parts: (1) a heterogeneous semantic slot graph, (2) a dual-encoder, and (3) a slot-aware decoder. ## conclusion in this work, we propose a semantic slot guided adversarial sequence-to-sequence network for abstractive dialogue summarization, which utilizes the semantic slot information to improve the model architecture and decoding algorithm via the slot-level mask cross-attention mechanism and slot-driven beam search. a contrastive learning with adversarial perturbations is also introduced to assist the training process. experiments demonstrated the effectiveness of our proposed models in terms of both readability and factualness.