Shaping Political Discourse using multi-source News Summarization
- URL: http://arxiv.org/abs/2312.11703v1
- Date: Mon, 18 Dec 2023 21:03:46 GMT
- Title: Shaping Political Discourse using multi-source News Summarization
- Authors: Charles Rajan, Nishit Asnani, Shreya Singh
- Abstract summary: We have developed a machine learning model that generates a concise summary of a topic from multiple news documents.
The model is designed to be unbiased by sampling its input equally from all the different aspects of the topic.
- Score: 0.46040036610482665
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-document summarization is the process of automatically generating a
concise summary of multiple documents related to the same topic. This summary
can help users quickly understand the key information from a large collection
of documents. Multi-document summarization systems are more complex than
single-document summarization systems due to the need to identify and combine
information from multiple sources. In this paper, we have developed a machine
learning model that generates a concise summary of a topic from multiple news
documents. The model is designed to be unbiased by sampling its input equally
from all the different aspects of the topic, even if the majority of the news
sources lean one way.
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) - Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles [136.84278943588652]
We propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event.
To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm.
The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference.
arXiv Detail & Related papers (2023-09-17T20:28:17Z) - 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) - PDSum: Prototype-driven Continuous Summarization of Evolving
Multi-document Sets Stream [33.68263291948121]
We propose a new summarization problem, Evolving Multi-Document sets stream Summarization (EMDS)
We introduce a novel unsupervised algorithm PDSum with the idea of prototype-driven continuous summarization.
PDSum builds a lightweight prototype of each multi-document set and exploits it to adapt to new documents.
arXiv Detail & Related papers (2023-02-10T23:43:46Z) - How "Multi" is Multi-Document Summarization? [15.574673241564932]
It is expected that both reference summaries in MDS datasets, as well as system summaries, would indeed be based on dispersed information.
We propose an automated measure for evaluating the degree to which a summary is disperse''
Our results show that certain MDS datasets barely require combining information from multiple documents, where a single document often covers the full summary content.
arXiv Detail & Related papers (2022-10-23T10:20:09Z) - Read Top News First: A Document Reordering Approach for Multi-Document
News Summarization [27.30854257540805]
We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them.
The reordering makes the salient content easier to learn by the summarization model.
arXiv Detail & Related papers (2022-03-19T06:01:11Z) - Modeling Endorsement for Multi-Document Abstractive Summarization [10.166639983949887]
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.
arXiv Detail & Related papers (2021-10-15T03:55:42Z) - MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News
Summarization [19.062996443574047]
We present a new dataset MiRANews and benchmark existing summarization models.
We show via data analysis that it's not only the models which are to blame.
assisted summarization reduces 55% of hallucinations when compared to single-document summarization models trained on the main article only.
arXiv Detail & Related papers (2021-09-22T10:58:40Z) - Text Summarization with Latent Queries [60.468323530248945]
We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms.
Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time.
Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
arXiv Detail & Related papers (2021-05-31T21:14:58Z) - 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) - From Standard Summarization to New Tasks and Beyond: Summarization with
Manifold Information [77.89755281215079]
Text summarization is the research area aiming at creating a short and condensed version of the original document.
In real-world applications, most of the data is not in a plain text format.
This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
arXiv Detail & Related papers (2020-05-10T14:59:36Z)
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.