Contrastive Hierarchical Discourse Graph for Scientific Document Summarization

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 202

HEGEL: Hypergraph Transformer for Long Document Summarization

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 202

SummIt: Iterative Text Summarization via ChatGPT

Existing text summarization systems have made significant progress in recent years but typically generates summaries in a single step. The one-shot summarization setting is sometimes inadequate, however, as the generated summary may contain hallucinations or overlook important details related to the reader's interests. In this paper, we address 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, closely resembling the iterative process humans undertake when drafting and revising summaries. We also explore using in-context learning to guide the rationale generation and summary refinement. Furthermore, we explore the potential benefits of integrating knowledge and topic extractors into the framework to enhance summary faithfulness and controllability. We evaluate the performance of our framework on three benchmark summarization datasets through empirical and qualitative analyses. We also conduct a human evaluation to validate the effectiveness of the model's refinements and find a potential issue of over-correction. Our code is available at \url{https://github.com/hpzhang94/summ_it}.Comment: work in progres

Extractive Summarization via ChatGPT for Faithful Summary Generation

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 ChatGPT has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. However, concerns regarding factuality and faithfulness have hindered its practical applications for summarization systems. 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's extractive summarization performance is still inferior to existing supervised systems in terms of ROUGE scores. 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 for faithful text summarization tasks using two-stage approaches.Comment: Work in progres

Deciphering protein glycosylation through novel mass spectrometry-based proteomic strategies

Protein glycosylation is essential for cell survival and proliferation. Comprehensive analysis of protein glycosylation can aid in a better understanding of protein functions, cellular activities, and the molecular mechanisms of diseases. Emerging mass spectrometry (MS)-based proteomics enables comprehensive analysis of protein glycosylation and many other types of modifications. However, due to the heterogeneity of glycans and the low abundance of many glycoproteins in complex biological samples, it is extraordinarily challenging to globally and site-specifically analyze glycoproteins. This thesis focuses on the development of new methods for global analysis of glycoproteins, and the applications of the newly developed methods for biomedical research. This thesis is constituted of six chapters. Chapter 1 is an overview of MS-based glycoproteomics analysis, with an emphasis on the endeavors in the literature to solve the two major problems for global analysis of glycoproteins mentioned above. This chapter retraces the developments of important chemical and enzymatic methods in this field, and includes the discussion regarding how these methods have enabled qualitative and quantitative analyses of glycoproteins in a variety of biological systems. Chapter 2 focuses on the development of a strategy that utilizes the universal recognition between boronic acid and sugars, in order to enrich glycopeptides for LC-MS/MS analysis. Chapter 3 shows the approach of achieving quantitative analysis of protein glycosylation through the combination of boronic acid enrichment and quantitative proteomics. Chapter 4 describes a strategy for cell-surface N-glycoproteome analysis. Metabolic labeling, click chemistry, and MS-based proteomics were combined to specifically map the glycoproteins located only on cell surface. The labeling efficiencies of different sugar analogs were compared, and this method was combined with either stable isotope labeling in cell culture (SILAC) or tandem mass tag (TMT)-labeling to quantitatively study the surface N-glycoproteins. Chapter 5 explains how protein S-GlcNAcylation was unexpectedly found in human cells. Starting with an attempt to profile protein O-GlcNAc, hundreds of S-GlcNAcylation sites were surprisingly identified on cysteine residues. This modification was demonstrated not to be caused by chemical reactions with the cleavable linker during sample preparation nor due to false site assignment. Furthermore, protein S-GlcNAcylation events were investigated with different sugar analog labeling in three cell lines. Chapter 6 features an application of MS-based proteomics in biomedical research. In this chapter, the cellular responses and pleiotropic effects in statin-treated cells on the proteome, glycoproteome, and phosphoproteome levels were analyzed. In addition to the independent projects discussed above, the collaborative projects about that investigation of the cellular mechanisms of gold-nanorod assisted cancer photothermal therapy, and the discordance between mRNA and proteome in ovarian cancer tissues were also conducted. The abstracts of the publications resulted from the collaborations are shown in the appendix. In conclusion, the work presented in this thesis majorly combines chemical biology and modern MS-based proteomics to study protein modifications, especially glycosylation. This thesis strives to advance the techniques of glycoproteomics and apply the state-of-the-art methods to investigate biological and biomedical problems.Ph.D

DiffuSum: Generation Enhanced Extractive Summarization with Diffusion

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 Finding

Simultaneous Quantitation of Glycoprotein Degradation and Synthesis Rates by Integrating Isotope Labeling, Chemical Enrichment, and Multiplexed Proteomics

Protein glycosylation is essential for cell survival and regulates many cellular events. Reversible glycosylation is also dynamic in biological systems. The functions of glycoproteins are regulated by their dynamics to adapt the ever-changing inter- and intracellular environments. Glycans on proteins not only mediate a variety of protein activities, but also creates a steric hindrance for protecting the glycoproteins from degradation by proteases. In this work, a novel strategy integrating isotopic labeling, chemical enrichment and multiplexed proteomics was developed to simultaneously quantify the degradation and synthesis rates of many glycoproteins in human cells. We quantified the synthesis rates of 847 N-glycoproteins and the degradation rates of 704 glycoproteins in biological triplicate experiments, including many important glycoproteins such as CD molecules. Through comparing the synthesis and degradation rates, we found that most proteins have higher synthesis rates since cells are still growing throughout the time course, while a small group of proteins with lower synthesis rates mainly participate in adhesion, locomotion, localization, and signaling. This method can be widely applied in biochemical and biomedical research and provide insights into elucidating glycoprotein functions and the molecular mechanism of many biological events

Global and Site-Specific Analysis Revealing Unexpected and Extensive Protein S‑GlcNAcylation in Human Cells

Protein glycosylation is highly diverse and essential for mammalian cell survival. Heterogeneous glycans may be bound to different amino acid residues, forming multiple types of protein glycosylation. In this work, unexpected protein S-GlcNAcylation on cysteine residues was observed to extensively exist in human cells through global and site-specific analysis of protein GlcNAcylation by mass spectrometry. Three independent experiments produced similar results of many cysteine residues bound to <i>N</i>-acetylglucosamine (GlcNAc). Among well-localized S-GlcNAcylation sites, several motifs with an acidic amino acid around the sites were identified, which strongly suggests that a particular type of enzyme is responsible for this modification. Clustering results show that glycoproteins modified with S-GlcNAc are mainly involved in cell–cell adhesion and gene expression. For the first time, we found that proteins were extensively bound to GlcNAc through the side chains of cysteine residues in human cells, and the current discovery further advances our understanding of protein glycosylation