| Challenge: | In Multi-Document Summarization, the input is a set of documents, and the output is its summary. |
| Approach: | They propose a novel pretraining objective that uses the ROUGE-based centroid of each document cluster as a proxy for its summary. |
| Outcome: | The proposed model is better or comparable to state-of-the-art models. |
Similar Papers
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. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
| Approach: | They formalize the task and bootstrap it using existing datasets, retrievers and summarizers. |
| Outcome: | The proposed method reduces the sensitivity of summarizers to imperfect retrieval, but is highly sensitive to other errors. |
Proposition-Level Clustering for Multi-Document Summarization (2022.naacl-main)
Copied to clipboard
Ori Ernst, Avi Caciularu, Ori Shapira, Ramakanth Pasunuru, Mohit Bansal, Jacob Goldberger, Ido Dagan
| Challenge: | Existing methods focused on clustering sentences to indicate information saliency and avoid redundancy. |
| Approach: | They propose to group together sub-sentential propositions to generate a representative sentence for each cluster via text fusion. |
| Outcome: | The proposed method improves over the previous state-of-the-art method in the DUC 2004 and TAC 2011 datasets, both in automatic ROUGE scores and human preference. |
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Multi-document summarization models are limited by limited references and with maximum-likelihood objectives. |
| Approach: | They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. |
| Outcome: | The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents . |
Corpora Evaluation and System Bias Detection in Multi-document Summarization (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-document summarization (MDS) is a task of combining multiple documents into a concise text paragraph. |
| Approach: | They propose to use a multi-document summarization task to reflect key points from any set of documents into a concise text paragraph. |
| Outcome: | The proposed system performs better on a set of selected datasets than on the other ones. |
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
Copied to clipboard
| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
Ant Colony System for Multi-Document Summarization (C18-1)
Copied to clipboard
| Challenge: | Existing methods for summarizing documents are greedy and greedy, but they rarely produce the best summaries. |
| Approach: | They propose an extractive multi-document summarization approach based on an ant colony system to optimize information coverage of summary sentences. |
| Outcome: | The proposed system achieves the best scores on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot . |
Coarse-to-Fine Query Focused Multi-Document Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on query focused multi-document summarization relies heavily on retrieval-style methods. |
| Approach: | They propose a query-cluster-based model which uses more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central. |
| Outcome: | The proposed framework outperforms strong comparison systems on benchmark datasets across domains and query types. |