Discovering possible candidate elements and capturing their connections to form the output triplets constitute the core challenge of aspect sentiment triplet extraction (ASTE). However, the information encapsulated by prevailing methods within a solitary sentence may often prove insufficient, particularly in complex scenarios characterized by uncommon aspect, opinion terms or intricate syntax patterns. To mitigate these limitations, we advocate incorporating inter-sentence information retrieval to enrich intra-sentence representations within ASTE. One existing study has proposed a method dubbed Retrieval-Based Aspect Sentiment Triplet Extraction via Label Interpolation (RLI), which retrieves triplets from the corpus to augment the representations of a candidate aspect-opinion pair and further improve sentiment prediction. Nevertheless, obtaining data with standard triplets might be challenging in practice. Therefore, we propose an approach, namely Multi-Task ASTE with the Corpus-Enhanced Graph (MACG), to conduct sentence-level retrieval and extract helpful information from unlabeled similar sentences. Specifically, we design a corpus-level enhanced graph to capture inter-sentence information, alongside a local graph preserving intra-sentence information. A graph neural network is subsequently employed to adaptively learn enhanced representations of the target sentence for ASTE. RLI and MACG collaboratively form a comprehensive methodological framework, which is effective in both scenarios with and without standard triplet labels. Extensive experiments on two benchmarks demonstrate the superiority and flexibility of retrieving inter-sentence information, which underscores their potential to advance ASTE by leveraging neighboring information.

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications (e.g., passage-based retrieval and high-quality information extraction). However, long documents, containing hundreds or even thousands of pages and a variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper position on the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. As for evaluations, we find that previous studies ignore the error that the depth of a node is correct while its path to the root is wrong. Since such mistakes may worsen the downstream applications seriously, a new measure is developed for a more careful evaluation. The empirical experiments based on thousands of long documents from Chinese financial market, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.972 6, 0.729 1 and 0.957 8 in the Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that the logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.