| Challenge: | Existing methods for generating comparative summaries that highlight similarities and contradictions in input documents are lacking large parallel training data for their training. |
| Approach: | They propose a method for generating comparative summaries that highlight similarities and contradictions in input documents by using a neural interpretation of traditional concept-to-text generation systems. |
| Outcome: | The proposed model is compared with conventional methods in the domain of nutrition and health, where the existing models lack large parallel training data. |
Similar Papers
Multi-doc Hybrid Summarization via Salient Representation Learning (2023.acl-industry)
Copied to clipboard
| Challenge: | Multi-document summarization is gaining more and more attention . extractive multi-doc approaches intend to directly extract key facts from multiple sources . |
| Approach: | They propose a multi-document hybrid summarization approach that generates a human-readable summary and extracts corresponding key evidences based on multi-doc inputs. |
| Outcome: | The proposed method generates a human-readable summary and extracts key evidences based on multi-doc inputs. |
Falsesum: Generating Document-level NLI Examples for Recognizing Factual Inconsistency in Summarization (2022.naacl-main)
Copied to clipboard
| Challenge: | Neural abstractive summarization models generate factually inconsistent summaries . previous work has introduced the task of recognizing factual inconsistency as a downstream application of natural language inference (NLI). |
| Approach: | They propose a data generation pipeline that enables a task-oriented approach to detect factual inconsistencies in abstractive summarization models. |
| Outcome: | The proposed model improves the state-of-the-art performance across four benchmarks for recognizing factual inconsistency in generated summaries. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to summarize documents in multiple languages are not systematically evaluated to ensure semantic coherence across target languages. |
| Approach: | They propose a principled re-ranking approach to ensure semantic coherence in documents in multiple target languages while ensuring semantic similarity across target languages. |
| Outcome: | The proposed model combines the difficulties of monolingual summarization with those of machine translation, such as translation of idiomatic expressions and cultural references. |
Document Summarization with Latent Queries (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing benchmarks for query-focused summarization are small for training large neural models. |
| Approach: | They propose a unified modeling framework for query-focused summarization . they model queries as discrete latent variables over document tokens . |
| Outcome: | The proposed framework outperforms strong comparison systems across benchmarks, query types, document settings, and target domains. |
Multilingual Summarization with Factual Consistency Evaluation (2023.findings-acl)
Copied to clipboard
| Challenge: | Abstractive summarization models generate factually inconsistent summaries, reducing their utility for real-world applications. |
| Approach: | They propose to use data filtering and controlled generation to detect hallucinations in machine generated summaries. |
| Outcome: | The proposed models detect factual inconsistencies in machine generated summaries, but they focus on English only. |
Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles (2020.emnlp-main)
Copied to clipboard
| Challenge: | Multi-XScience is a dataset construction protocol that favours abstractive modeling approaches. |
| Approach: | They propose a large-scale multi-document summarization dataset that is based on articles and lexical databases and WordNet synonymy information to generate related-work sections of a paper. |
| Outcome: | The proposed method is based on lexical databases and WordNet synonymy information to write related work sections of a paper based upon their abstract and the articles they reference. |
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization (2020.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of high quality multilingual data for cross-lingual summarization is a costly endeavor since it requires humans to read, comprehend, condense, and paraphrase entire articles. |
| Approach: | They propose to use a large-scale, multilingual dataset to evaluate cross-lingual abstractive summarization systems. |
| Outcome: | The proposed method significantly outperforms baseline approaches while being more cost efficient during inference. |
PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for abstractive multi-document summarization fail to generate concise, reflective summaries. |
| Approach: | They propose a pre-trained abstractive multi-document summarization model that uses unlabeled multi-doctoral inputs to generate concise, reflective summaries. |
| Outcome: | The proposed model outperforms competing models on a wide range of MDS datasets. |
Promoting Topic Coherence and Inter-Document Consorts in Multi-Document Summarization via Simplicial Complex and Sheaf Graph (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing systems that generate summaries from multiple sources often lack accuracy and accuracy due to the length of tokens used in encoding. |
| Approach: | They propose a novel encoder-decoder model that uses pre-trained BART to analyze linguistic nuances, simplicial complex layer to apprehend inherent properties that transcend pairwise associations and sheaf graph attention to effectively capture heterophilic properties. |
| Outcome: | The proposed model achieves consistent performance improvement across all evaluation metrics (syntactical, semantical and faithfulness). |