Modeling Endorsement for Multi-Document Abstractive Summarization
- URL: http://arxiv.org/abs/2110.07844v1
- Date: Fri, 15 Oct 2021 03:55:42 GMT
- Title: Modeling Endorsement for Multi-Document Abstractive Summarization
- Authors: Logan Lebanoff and Bingqing Wang and Zhe Feng and Fei Liu
- Abstract summary: A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s)
In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization.
Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents.
- Score: 10.166639983949887
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A crucial difference between single- and multi-document summarization is how
salient content manifests itself in the document(s). While such content may
appear at the beginning of a single document, essential information is
frequently reiterated in a set of documents related to a particular topic,
resulting in an endorsement effect that increases information salience. In this
paper, we model the cross-document endorsement effect and its utilization in
multiple document summarization. Our method generates a synopsis from each
document, which serves as an endorser to identify salient content from other
documents. Strongly endorsed text segments are used to enrich a neural
encoder-decoder model to consolidate them into an abstractive summary. The
method has a great potential to learn from fewer examples to identify salient
content, which alleviates the need for costly retraining when the set of
documents is dynamically adjusted. Through extensive experiments on benchmark
multi-document summarization datasets, we demonstrate the effectiveness of our
proposed method over strong published baselines. Finally, we shed light on
future research directions and discuss broader challenges of this task using a
case study.
Related papers
- Unified Multi-Modal Interleaved Document Representation for Information Retrieval [57.65409208879344]
We produce more comprehensive and nuanced document representations by holistically embedding documents interleaved with different modalities.
Specifically, we achieve this by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation.
arXiv Detail & Related papers (2024-10-03T17:49:09Z) - Peek Across: Improving Multi-Document Modeling via Cross-Document
Question-Answering [49.85790367128085]
We pre-training a generic multi-document model from a novel cross-document question answering pre-training objective.
This novel multi-document QA formulation directs the model to better recover cross-text informational relations.
Unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation and long text generation.
arXiv Detail & Related papers (2023-05-24T17:48:40Z) - Mining both Commonality and Specificity from Multiple Documents for
Multi-Document Summarization [1.4629756274247374]
The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents.
This paper proposes a multi-document summarization approach based on hierarchical clustering of documents.
arXiv Detail & Related papers (2023-03-05T14:25:05Z) - Learning Diverse Document Representations with Deep Query Interactions
for Dense Retrieval [79.37614949970013]
We propose a new dense retrieval model which learns diverse document representations with deep query interactions.
Our model encodes each document with a set of generated pseudo-queries to get query-informed, multi-view document representations.
arXiv Detail & Related papers (2022-08-08T16:00:55Z) - ACM -- Attribute Conditioning for Abstractive Multi Document
Summarization [0.0]
We propose a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary.
This approach shows strong gains in ROUGE score over baseline multi document summarization approaches.
arXiv Detail & Related papers (2022-05-09T00:00:14Z) - Unsupervised Summarization with Customized Granularities [76.26899748972423]
We propose the first unsupervised multi-granularity summarization framework, GranuSum.
By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner.
arXiv Detail & Related papers (2022-01-29T05:56:35Z) - iFacetSum: Coreference-based Interactive Faceted Summarization for
Multi-Document Exploration [63.272359227081836]
iFacetSum integrates interactive summarization together with faceted search.
Fine-grained facets are automatically produced based on cross-document coreference pipelines.
arXiv Detail & Related papers (2021-09-23T20:01:11Z) - WSL-DS: Weakly Supervised Learning with Distant Supervision for Query
Focused Multi-Document Abstractive Summarization [16.048329028104643]
In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents.
One major challenge for this task is the lack of availability of labeled training datasets.
We propose a novel weakly supervised learning approach via utilizing distant supervision.
arXiv Detail & Related papers (2020-11-03T02:02:55Z) - Enhancing Extractive Text Summarization with Topic-Aware Graph Neural
Networks [21.379555672973975]
This paper proposes a graph neural network (GNN)-based extractive summarization model.
Our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection.
The experimental results demonstrate that our model achieves substantially state-of-the-art results on CNN/DM and NYT datasets.
arXiv Detail & Related papers (2020-10-13T09:30:04Z) - Leveraging Graph to Improve Abstractive Multi-Document Summarization [50.62418656177642]
We develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents.
Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents.
Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries.
arXiv Detail & Related papers (2020-05-20T13:39:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.