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Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization ability of summarization models. To address this issue, we introduce DomainSum, a hierarchical benchmark designed to capture fine-grained domain shifts in abstractive summarization. We categorize these shifts into three levels: genre, style, and topic, and demonstrate through comprehensive benchmark analysis that they follow a hierarchical structure. Furthermore, we evaluate the domain generalization capabilities of commonly used pre-trained language models (PLMs) and large language models (LLMs) in in-domain and cross-domain settings.

Biomedical Event Extraction (BEE) is a challenging task that involves modeling complex relationships between fine-grained entities in biomedical text. BEE has traditionally been formulated as a classification problem. With the recent technological advancements in large language models (LLMs), generation-based models that cast event extraction as a sequence generation problem have attracted much attention from the NLP research communities. However, current generative models often overlook the importance of cross-instance information from complex event structures such as nested events and overlapping events, which contribute to over 20% of the events in the benchmark datasets. In this paper, we propose an event structure-aware generative model named GenBEE, which can capture complex event structures in biomedical text for biomedical event extraction. In particular, GenBEE constructs event prompts that distill knowledge from LLMs for incorporating both label semantics and argument dependency relationships into the proposed model. In addition, GenBEE also generates prefixes with event structural prompts to incorporate structural features for improving the model's overall performance. We have evaluated the proposed GenBEE model on three widely used biomedical event extraction benchmark datasets, namely MLEE, GE11, and PHEE. Experimental results show that GenBEE has achieved state-of-the-art performance on the MLEE and GE11 datasets, and achieved competitive results when compared to the state-of-the-art classification-based models on the PHEE dataset., Comment: 8 pages, 4 figures, 6 tables

Recent research has explored distilling knowledge from large language models (LLMs) to optimize retriever models, especially within the retrieval-augmented generation (RAG) framework. However, most existing training methods rely on extracting supervision signals from LLMs' weights or their output probabilities, which is not only resource-intensive but also incompatible with black-box LLMs. In this paper, we introduce \textit{Intermediate Distillation}, a data-efficient knowledge distillation training scheme that treats LLMs as black boxes and distills their knowledge via an innovative LLM-ranker-retriever pipeline, solely using LLMs' ranking generation as the supervision signal. Extensive experiments demonstrate that our proposed method can significantly improve the performance of retriever models with only 1,000 training instances. Moreover, our distilled retriever model significantly boosts performance in question-answering tasks within the RAG framework, demonstrating the potential of LLMs to economically and effectively train smaller models., Comment: 13 pages, 7 figures, 3 tables

Text summarization research has undergone several significant transformations with the advent of deep neural networks, pre-trained language models (PLMs), and recent large language models (LLMs). This survey thus provides a comprehensive review of the research progress and evolution in text summarization through the lens of these paradigm shifts. It is organized into two main parts: (1) a detailed overview of datasets, evaluation metrics, and summarization methods before the LLM era, encompassing traditional statistical methods, deep learning approaches, and PLM fine-tuning techniques, and (2) the first detailed examination of recent advancements in benchmarking, modeling, and evaluating summarization in the LLM era. By synthesizing existing literature and presenting a cohesive overview, this survey also discusses research trends, open challenges, and proposes promising research directions in summarization, aiming to guide researchers through the evolving landscape of summarization research., Comment: 30 pages, 8 figures, 6 tables

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024/., Comment: CVPR 2024 Mobile Intelligent Photography and Imaging (MIPI) Workshop--Nighttime Flare Removal Challenge Report. Website: https://mipi-challenge.org/MIPI2024/

Retrieval-augmented generation framework can address the limitations of large language models by enabling real-time knowledge updates for more accurate answers. An efficient way in the training phase of retrieval-augmented models is attention distillation, which uses attention scores as a supervision signal instead of manually annotated query-document pairs. Despite its growing popularity, the detailed mechanisms behind the success of attention distillation remain unexplored, particularly the specific patterns it leverages to benefit training. In this paper, we address this gap by conducting a comprehensive review of attention distillation workflow and identifying key factors influencing the learning quality of retrieval-augmented language models. We further propose indicators for optimizing models' training methods and avoiding ineffective training., Comment: 10 pages, 8 figures

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models (ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We quantitatively analyze the distinctions and resemblances between human journalists and LLM in assessing news subject credibility, news creator credibility, time-sensitive, and political framing. Our findings highlight LLM's potential to serve as a preliminary screening method, offering a promising avenue to mitigate the inherent biases of human journalists and enhance fake news detection.

Synthetic aperture radar (SAR) image change detection is a critical task and has received increasing attentions in the remote sensing community. However, existing SAR change detection methods are mainly based on convolutional neural networks (CNNs), with limited consideration of global attention mechanism. In this letter, we explore Transformer-like architecture for SAR change detection to incorporate global attention. To this end, we propose a convolution and attention mixer (CAMixer). First, to compensate the inductive bias for Transformer, we combine self-attention with shift convolution in a parallel way. The parallel design effectively captures the global semantic information via the self-attention and performs local feature extraction through shift convolution simultaneously. Second, we adopt a gating mechanism in the feed-forward network to enhance the non-linear feature transformation. The gating mechanism is formulated as the element-wise multiplication of two parallel linear layers. Important features can be highlighted, leading to high-quality representations against speckle noise. Extensive experiments conducted on three SAR datasets verify the superior performance of the proposed CAMixer. The source codes will be publicly available at https://github.com/summitgao/CAMixer ., Comment: Accepted by IEEE GRSL

Text editing is a crucial task of modifying text to better align with user intents. However, existing text editing benchmark datasets contain only coarse-grained instructions and lack explainability, thus resulting in outputs that deviate from the intended changes outlined in the gold reference. To comprehensively investigate the text editing capabilities of large language models (LLMs), this paper introduces XATU, the first benchmark specifically designed for fine-grained instruction-based explainable text editing. XATU considers finer-grained text editing tasks of varying difficulty (simplification, grammar check, fact-check, etc.), incorporating lexical, syntactic, semantic, and knowledge-intensive edit aspects. To enhance interpretability, we combine LLM-based annotation and human annotation, resulting in a benchmark that includes fine-grained instructions and gold-standard edit explanations. By evaluating existing LLMs against our benchmark, we demonstrate the effectiveness of instruction tuning and the impact of underlying architecture across various editing tasks. Furthermore, extensive experimentation reveals the significant role of explanations in fine-tuning language models for text editing tasks. The benchmark will be open-sourced to support reproduction and facilitate future research at~\url{https://github.com/megagonlabs/xatu}., Comment: LREC-COLING 2024

Multi-document summarization aims to obtain core information from a collection of documents written on the same topic. This paper proposes a new holistic framework for unsupervised multi-document extractive summarization. Our method incorporates the holistic beam search inference method associated with the holistic measurements, named Subset Representative Index (SRI). SRI balances the importance and diversity of a subset of sentences from the source documents and can be calculated in unsupervised and adaptive manners. To demonstrate the effectiveness of our method, we conduct extensive experiments on both small and large-scale multi-document summarization datasets under both unsupervised and adaptive settings. The proposed method outperforms strong baselines by a significant margin, as indicated by the resulting ROUGE scores and diversity measures. Our findings also suggest that diversity is essential for improving multi-document summary performance., Comment: Findings of IJCNLP-AACL 2023

The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization. CHANGES represents a scientific paper with a hierarchical discourse graph and learns effective sentence representations with dedicated designed hierarchical graph information aggregation. We also propose a graph contrastive learning module to learn global theme-aware sentence representations. Extensive experiments on the PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the importance of capturing hierarchical structure information in modeling scientific papers., Comment: CODI at ACL 2023

Text summarization systems have made significant progress in recent years, but typically generate summaries in one single step. However, the one-shot summarization setting is sometimes inadequate, as the generated summary may contain hallucinations or overlook essential details related to the reader's interests. This paper addresses this limitation by proposing SummIt, an iterative text summarization framework based on large language models like ChatGPT. Our framework enables the model to refine the generated summary iteratively through self-evaluation and feedback, resembling humans' iterative process when drafting and revising summaries. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We automatically evaluate the performance of our framework on three benchmark summarization datasets. We also conduct a human evaluation to validate the effectiveness of the iterative refinements and identify a potential issue of over-correction., Comment: Findings of EMNLP 2023

Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by making individual sentence label predictions. This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generating the desired summary sentence representations with diffusion models and extracting sentences based on sentence representation matching. In addition, DiffuSum jointly optimizes a contrastive sentence encoder with a matching loss for sentence representation alignment and a multi-class contrastive loss for representation diversity. Experimental results show that DiffuSum achieves the new state-of-the-art extractive results on CNN/DailyMail with ROUGE scores of $44.83/22.56/40.56$. Experiments on the other two datasets with different summary lengths also demonstrate the effectiveness of DiffuSum. The strong performance of our framework shows the great potential of adapting generative models for extractive summarization. To encourage more following work in the future, we have released our codes at \url{https://github.com/hpzhang94/DiffuSum}, Comment: ACL 2023 Findings

Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of large language models has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. This paper first presents a thorough evaluation of ChatGPT's performance on extractive summarization and compares it with traditional fine-tuning methods on various benchmark datasets. Our experimental analysis reveals that ChatGPT exhibits inferior extractive summarization performance in terms of ROUGE scores compared to existing supervised systems, while achieving higher performance based on LLM-based evaluation metrics. In addition, we explore the effectiveness of in-context learning and chain-of-thought reasoning for enhancing its performance. Furthermore, we find that applying an extract-then-generate pipeline with ChatGPT yields significant performance improvements over abstractive baselines in terms of summary faithfulness. These observations highlight potential directions for enhancing ChatGPT's capabilities in faithful summarization using two-stage approaches., Comment: Findings of EMNLP 2023

With the calculated guiding center radius $R_{guiding}$ and birth radius $R_{birth}$, we investigate the role of radial migration on the description of lithium evolution in the Galactic disk based on the upper envelope of the A(Li) vs. [Fe/H] diagram. Using migration distances, we find that stars in the solar neighborhood are born at different locations in the galactic disk, and cannot all be explained by models of chemical evolution in the solar neighborhood. It is found that the upper envelope of the A(Li) vs. [Fe/H] diagram varies significantly with $R_{birth}$, which explains the decrease of Li for super-metal-rich (SMR) stars because they are non-young stars born in the inner disk. The upper envelope of Li-$R_{birth}$ plane fits very well with chemical evolution models by Grisoni et al. for $R_{birth} = 7 - 12$ kpc, outside which young stars generally lack sufficient time to migrate to the solar neighborhood. For stars born in the solar neighborhood, the young open clusters and the upper envelope of field stars with age $<$ 3 Gyr fit well with theoretical prediction. We find that calculations using stars with ages less than 3 Gyr are necessary to obtain an undepleted Li upper envelope, and that stars with solar age (around 4.5 Gyr) have depleted around 0.3 dex from the original value based on the chemical evolution model of Grisoni et al., Comment: 7 pages, 5 figures, accepted for publication in MNRAS

Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL., Comment: EMNLP 2022

Super metal-rich (SMR) stars in the solar neighborhood are thought to be born in the inner disk and came to present location by radial migration, which is most intense at the co-rotation resonance (CR) of the Galactic bar. In this work, we show evidence for the CR origin of SMR stars in LAMOST-Gaia by detecting six ridges and undulations in the phi versus Lz space coded by median VR, following a similar slope of -8 km/s kpc/deg. The slope is predicted by Monario et al.'s model for CR of a large and slow Galactic bar. For the first time, we show the variation of angular momentum with azimuths from -10 deg to 20 deg for two outer and broad undulations with negative VR around -18 km/s following this slope. The wave-like pattern with large amplitude outside CR and a wide peak of the second undulations indicate that minor merger of the Sagittarius dwarf galaxy with the disk might play a role besides the significant impact of CR of the Galactic bar., Comment: 5 pages, 3 figures, published in ApJ Letter

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameter, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show our method outperforms other anti-rotation methods and achieves great improvements in dermoscopy image classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images.

Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more prone to hallucinate facts that are unfaithful to the input context. In this paper, we propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control (ECC). We first compute entity coverage precision and prepend the corresponding control code for each training example, which implicitly guides the model to recognize faithfulness contents in the training phase. We further extend our method via intermediate fine-tuning on large but noisy data extracted from Wikipedia to unlock zero-shot summarization. We show that the proposed method leads to more faithful and salient abstractive summarization in supervised fine-tuning and zero-shot settings according to our experimental results on three benchmark datasets XSum, Pubmed, and SAMSum of very different domains and styles., Comment: NAACL 2022 findings

Radial migration is an important process in the evolution of the Galactic disk. The metallicity gradient of open clusters and its outliers provide an effective way to probe for this process. In this work, we compile metallicity, age, and kinematic parameters for 225 open clusters and carry out a quantitative analysis of radial migration via the calculated migration distances. Based on clusters with age $< 0.5$ Gyr, we obtain the present-day metallicity gradient of $-0.074 \pm 0.007$ dex/kpc. Along this gradient distributes three sequences, and clusters in the upper, the middle, and the lower groups are found to be old outward-migrators, in-situ clusters, and inward-migrators, respectively. The migration distance increases with age, but its most effective time is probably less than 3 Gyr. The metallicity gradient breaks out at $R_g$ (guiding center radius) $\sim11.5$ kpc, which is caused by the lack of young open clusters in the outer disk and the presence of old outward-migrators in the upper sequence. It shows that this boundary is related to the different effects of radial migration between the inner and outer disks. We also found many special open clusters in and near the outer disk of $R > 11$ kpc and a steeper metallicity gradient from the inner disk of $R_g < 7$ kpc, which tells a complicated evolution history of the Galactic disk by different effects of stellar radial migration., Comment: 19 pages, 11 figures, accepted for publication in the Astrophysical Journal

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in the domain of histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. While, it is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database that consists of histopathological whole slide images (WSIs) for a query ROI. In this paper, we propose a novel framework for regions retrieval from WSI-database based on hierarchical graph convolutional networks (GCNs) and Hash technique. Compared to the present CBHIR framework, the structural information of WSI is preserved through graph embedding of GCNs, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the hierarchical GCN structures, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist defining query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on Hash technique, which ensures the framework is efficient and thereby adequate for practical large-scale WSI-database. The proposed method was validated on two public datasets for histopathological WSI analysis and compared to the state-of-the-art methods. The proposed method achieved mean average precision above 0.857 on the ACDC-LungHP dataset and above 0.864 on the Camelyon16 dataset in the irregular region retrieval tasks, which are superior to the state-of-the-art methods. The average retrieval time from a database within 120 WSIs is 0.802 ms.

Unsupervised document summarization has re-acquired lots of attention in recent years thanks to its simplicity and data independence. In this paper, we propose a graph-based unsupervised approach for extractive document summarization. Instead of ranking sentences by salience and extracting sentences one by one, our approach works at a summary-level by utilizing graph centrality and centroid. We first extract summary candidates as subgraphs based on centrality from the sentence graph and then select from the summary candidates by matching to the centroid. We perform extensive experiments on two bench-marked summarization datasets, and the results demonstrate the effectiveness of our model compared to state-of-the-art baselines., Comment: We found some error in Table 2. We need some time to update the paper

We report the detection of a large sample of high-$\alpha$-metal-rich stars on the low giant branch with $2.60.2$ than the comparison group. These properties strongly indicate its connection with the outer bar/bulge region at $R=3-5$ kpc. A tentative interpretation of this special group is that its stars were formed in the X-shaped bar/bulge region, close to its corotation radius, where radial migration is the most intense, and brings them to present locations at 9 kpc and beyond. Low eccentricities and slightly outward radial excursions of its stars are consistent with this scenario. Its kinematics (cold) and chemistry ($[\alpha/Fe]$ $\sim 0.1$) further support the formation of the instability-driven X-shaped bar/bulge from the thin disk., Comment: 13 pages, 8 figures, accepted for publication in Research in Astronomy and Astrophysics