Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing methods for summarizing documents rely on hand-crafted features or additional annotated data. |
| Approach: | They propose a method that makes use of two types of sentence embeddings . the method uses universal embeddable and domain-specific embeddible features . |
| Outcome: | The proposed method achieves competitive results on two types of summary, consisting of 665 bytes and 100 words. |
Similar Papers
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them. |
| Approach: | They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular benchmark datasets. |
Cross-lingual Sentence Embedding using Multi-Task Learning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing multilingual sentence embedding models require large parallel corpora to learn efficiently, limiting their scope. |
| Approach: | They propose a sentence embedding framework based on an unsupervised loss function . they capture semantic similarity and relatedness between sentences using a multi-task loss function. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on STS, BUCC and Tatoeba benchmarks and on a monolingual benchmark. |
Multi-Sentence Argument Linking (2020.acl-main)
Copied to clipboard
| Challenge: | Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking. |
| Approach: | They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution. |
| Outcome: | The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types. |
Incorporating Global Contexts into Sentence Embedding for Relational Extraction at the Paragraph Level with Distant Supervision (L18-1)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction (RE) only extract relations from sentences that contain two target entities. |
| Approach: | They propose to incorporate global contexts from paragraph-into-sentence embedding into RE . they propose to use a knowledge base to extract relations between pairs of entities . |
| Outcome: | The proposed approach can learn an exact RE from sentences without syntactic parsing. |
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Sentence embedding is essential for many NLP tasks, but reliance on manual labels limits scalability. |
| Approach: | They propose a method for controlling the generation direction of large language models in the latent space by integrating ranking information and semantic information. |
| Outcome: | The proposed method achieves new SOTA performance with a modest cost in ranking sentence synthesis. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
Copied to clipboard
| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
| Outcome: | The proposed methods produce document-level representations from sentences in 8 languages . the results show that a clever combination of sentence embeddings is usually better than encoding the full document as a single unit. |
BERT Has More to Offer: BERT Layers Combination Yields Better Sentence Embeddings (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks. |
| Approach: | They propose to combine certain layers of a BERT-based model rested on the data set and model to achieve substantially better results. |
| Outcome: | The proposed method outperforms baseline models on seven semantic textual similarity datasets and on eight transfer data sets. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
Copied to clipboard
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Exploring Multilingual Syntactic Sentence Representations (D19-55)
Copied to clipboard
| Challenge: | Recent studies on language models that learn syntactic information focus on learning the semantic structures of language. |
| Approach: | They propose to use a multilingual parallel corpus augmented by universal part-of-speech tags to learn syntactic sentence embeddings. |
| Outcome: | The proposed method performs better than state-of-the-art language models in low-resource languages. |
Graph-Augmented Open-Domain Multi-Document Summarization (2025.coling-industry)
Copied to clipboard
| Challenge: | Existing methods for summarizing documents neglect the relationships between documents . existing methods treat retrieval and summarization as separate tasks . |
| Approach: | They propose a framework that captures global document relationships through graph-based clustering . this cluster-level thematic information is then used to guide large language models . |
| Outcome: | The proposed framework significantly improves retrieval accuracy and produces better summaries than existing methods. |