Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022)

Abstract: A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.

## introduction multi-document news extractive summarization (mds) aims to extract the salient information from multiple related news documents into a concise summary. some approaches use task-specific architectures for this problem. for example, @xcite organize multiple documents as a heterogeneous graph before summarizing them. @xcite formulate the extractive summarization task as a semantic matching problem. recent works also explored reformulating this problem as a single-document summarization (sds) problem by concatenating all documents into a single meta-document and then using an sds model to summarize it @xcite @xcite . due to the conventions of news writing @xcite , salient information often appears at the beginning of a news article. as a result, many summarization systems, including recent neural models @xcite , pay more attention to the beginning of the document. therefore, in mds, it is important to consider the order in which the docu- * equal contribution ments are concatenated to form the meta-document before applying the summarization model. specifically, we argue that the various documents in the input are not equally important. some documents contain more salient or detailed information and are more important. therefore, compared with concatenating documents in an arbitrary order, it would be beneficial to reorder the documents such that the important ones are in the front of the meta-document and it becomes easier for the summarization model to learn the salient content. motivated by these factors, we propose a simple yet effective approach to reorder the input documents according to their relative importance before applying a summarization model. we evaluate the effectiveness of our approach on multi-news @xcite and duc-2004. 1 results show that our simple reordering approach significantly outperforms the state-of-the-art methods with more complex model architectures. we also observe that this approach brings more performance gain with the increase in the number of input documents. ## method we refer to d as a meta-document of m documents {d 1 , . . . , d m } with n sentences {s 1 , ..., s n } in total. the goal in extractive summarization is to extract a subset of sentences in d to summarize the input documents. it is usually formulated as a binary sentence classification problem, where each sentence is assigned a {0, 1} label to determine if it is to be included in the summary. below, we introduce our document reordering approach, and then the base summarization model. ## experiments in this section, we evaluate our document reordering based summarization approach. foot_2 ## conclusion in this work, we propose a document reordering based approach for multi-document news summarization. we rearrange the documents according to their relative importance while concatenating them into a meta-document and then apply a summarization model. our simple yet effective approach outperforms the baselines on two multi-document summarization datasets, demonstrating that document reordering is a promising direction for multidocument news summarization. a next step, which we leave for future work, is to explore the scalability of such approaches on large document clusters. rada mihalcea and paul tarau. 2004. textrank: bringing order into text. in proceedings of the 2004 conference on empirical methods in natural language processing, pages 404-411, barcelona, spain. association for computational linguistics. ramesh nallapati, bowen zhou, cicero dos santos, çaglar gulçehre, and bing xiang. 2016. abstractive text summarization using sequence-to-sequence rnns and beyond. in proceedings of the 20th signll conference on computational natural language learning, pages 280-290, berlin, germany. association for computational linguistics. danqing wang, pengfei liu, yining zheng, xipeng qiu, and xuanjing huang. 2020. heterogeneous graph neural networks for extractive document summarization. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 6209-6219, online. association for computational linguistics. xingxing zhang, furu wei, and ming zhou. 2019. hi-bert: document level pre-training of hierarchical bidirectional transformers for document summarization. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 5059-5069, florence, italy. association for computational linguistics. hao zheng and mirella lapata. 2019. sentence centrality revisited for unsupervised summarization. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 6236-6247, florence, italy. association for computational linguistics. ming zhong, pengfei liu, yiran chen, danqing wang, xipeng qiu, and xuanjing huang. 2020. extractive summarization as text matching. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 6197-6208, online. association for computational linguistics. ming zhong, pengfei liu, danqing wang, xipeng qiu, and xuanjing huang. 2019. searching for effective neural extractive summarization: what works and what's next. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 1049-1058, florence, italy. association for computational linguistics.