Papers with document representation
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring (P19-2)
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| Challenge: | Existing document embedding approaches focus on capturing sequences of words in documents . however, some document classification and regression tasks need to consider discourse structure of text . |
| Approach: | They propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require expensive parsers or annotation. |
| Outcome: | The proposed method improves essay Organization scoring and Argument Strength scoring. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings (C18-1)
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| Challenge: | Existing approaches focus on text information, but authors and overall ratings are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects. |
| Approach: | They propose a hierarchical user-aspect rating network model to consider user preference and overall ratings jointly. |
| Outcome: | The proposed model can predict aspects of a product in two real-world datasets. |
Unified Multimodal Interleaved Document Representation for Retrieval (2026.findings-eacl)
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| Challenge: | Existing methods focus on textual content, ignoring the fact that documents can contain multiple modalities. |
| Approach: | They propose a method that holistically embeds documents interleaved with multiple modalities . they use vision-language models that combine text, images, and tables into a unified format . |
| Outcome: | The proposed method outperforms baselines on textual and multimodal queries. |
Early Text Classification Using Multi-Resolution Concept Representations (N18-1)
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| Challenge: | e-communications have been misused by cyber-criminals, who hide in the depths of the web. |
| Approach: | They propose a document representation which allows us to generate multiple "views" of the analyzed text. |
| Outcome: | The proposed representation outperforms existing models in two tasks where anticipation is critical: sexual predator detection and depression detection. |
KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding (2023.acl-long)
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| Challenge: | Existing approaches to infuse knowledge graphs with pre-trained LMs are limited by the input sequence length. |
| Approach: | They propose a language model that leverages knowledge in local, document-level, and global contexts for long document understanding. |
| Outcome: | The proposed model achieves state-of-the-art on three long document understanding tasks across 6 datasets/settings. |
Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)
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| Challenge: | Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents. |
| Approach: | They propose a graph-based model that captures factual structures of documents for deepfake detection. |
| Outcome: | The proposed model improves strong base models built with RoBERTa on two public deepfake datasets. |
Stance Detection with Hierarchical Attention Network (C18-1)
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| Challenge: | Recent studies have focused on document-level opinion mining, but linguistic information is correlated with the stance of the document. |
| Approach: | They propose a hierarchical attention neural model to employ various linguistic information to construct the document representation. |
| Outcome: | The proposed model can detect stance of documents on two datasets. |
Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations (2025.naacl-long)
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Tung Nguyen, Tue Le, Hoang Tran Vuong, Quang Duc Nguyen, Duc Anh Nguyen, Linh Ngo Van, Sang Dinh, Thien Huu Nguyen
| Challenge: | Recent advances in topic models have significantly enhanced the performance compared to conventional probabilistic approaches. |
| Approach: | They propose to use a topic model optimization method to improve generalization and performance. |
| Outcome: | The proposed method improves topic models on popular benchmark datasets. |
X-Class: Text Classification with Extremely Weak Supervision (2021.naacl-main)
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| Challenge: | Weak supervision is a problem in text classification, but it requires corpusspecific knowledge. |
| Approach: | They propose a framework for extremely weak supervision that can be used to train a text classifier. |
| Outcome: | The proposed framework outperforms seed-driven weakly supervised methods on 7 benchmark datasets. |
Iterative Document Representation Learning Towards Summarization with Polishing (D18-1)
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| Challenge: | Existing summarization methods read through document only once to generate a document representation, resulting in a sub-optimal representation. |
| Approach: | They propose an iterative model for supervised extractive text summarization which polishes the document representation on many passes through the document. |
| Outcome: | The proposed model outperforms state-of-the-art extractive systems on CNN/DailyMail and DUC2002 datasets. |
BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)
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| Challenge: | Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text. |
| Approach: | They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. |
| Outcome: | The proposed framework improves document representation and summary generation process by leveraging the graph structure. |
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation (2021.acl-long)
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| Challenge: | Existing related work generation models are inflexible and extract sentences from multiple papers to form a related work discussion. |
| Approach: | They propose a Relation-aware Related work generator which generates an abstractive related work from the given multiple scientific papers in the same research area. |
| Outcome: | The proposed model improves over existing models and can be used to familiarize researchers with the state of the art in the field. |
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion (2023.emnlp-main)
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| Challenge: | Experimental results show that dense retrieval models are better at obtaining query-informed representations. |
| Approach: | They propose a dual-encoder approach that computes latent representations of query and document independently, but inference replaces the real query with a generated one. |
| Outcome: | The proposed approach outperforms previous dense retrieval models on in-domain and out-of-domain datasets. |
Hybrid Hierarchical Retrieval for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Recent work shows that dense hierarchical retrieval (DHR) can outperform dense passage retrieval. |
| Approach: | They propose a framework that applies sparse, dense and a combination of them to document and passage retrieval. |
| Outcome: | The proposed framework can outperform dense hierarchical retrieval (DHR) and sparse retrievers (BM25) on open-domain question answering (ODQA) datasets with an average improvement of 4.69% on recall@100 over DHR. |
Pseudo-Relevance for Enhancing Document Representation (2022.emnlp-main)
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| Challenge: | a novel approach to document retrieval can be used to encode documents as vectors . a few query-relevant terms can be pruned out to reduce index overhead . |
| Approach: | They propose to enhance the document representation for the bi-encoder approach in dense document retrieval. |
| Outcome: | The proposed solution reduces latency and memory footprint up to 8- and 3-fold . it is validated on MSMARCO and real-world search query logs . |
Intelligent Document Parsing: Towards End-to-end Document Parsing via Decoupled Content Parsing and Layout Grounding (2025.findings-emnlp)
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| Challenge: | Existing methods fragment document parsing into pipeline of separated subtasks, resulting in incomplete semantics and error propagation. |
| Approach: | They propose an end-to-end document parsing framework that leverages vision-language priors of MLLMs. |
| Outcome: | The proposed method surpasses existing methods significantly in document parsing . it leverages the vision-language priors of MLLMs to decouple parse and layout grounding based on visual information. |