https://github.com/icannos/glimpse-mds
공식 코드GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews
Papers with Code 아카이브
Multi-Document Summarization
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Multi-Document Summarization is a process of representing a set of documents with a short piece of text by capturing the relevant information and filtering out the redundant information. Two prominent approaches to Multi-Document Summarization are extractive and abstractive summarization. Extractive summarization systems aim to extract salient snippets, sentences or passages from documents, while abstractive summarization systems aim to concisely paraphrase the content of the documents.
Source: Multi-Document Summarization using Distributed Bag-of-Words Model
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jacob-parnell-rozetta/longformer_coverage
공식 코드A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization
https://github.com/johpro/elske
공식 코드https://github.com/jonas-becker/text-generation
공식 코드Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
https://github.com/jpeper/pelms
공식 코드PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization
https://github.com/knot-fit-but/oapapers
공식 코드https://github.com/knot-fit-but/oapapersviewer
공식 코드https://github.com/leehaoyuan/coverage_fairness
공식 코드https://github.com/liatschiff/openasp
공식 코드OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
https://github.com/lovodkin93/attribute-first-then-generate
공식 코드Attribute First, then Generate: Locally-attributable Grounded Text Generation
https://github.com/mediatechnologycenter/spider
공식 코드https://github.com/meetdavidwan/generationprograms
공식 코드GenerationPrograms: Fine-grained Attribution with Executable Programs
https://github.com/megagonlabs/hallucination_mds
공식 코드From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization
https://github.com/mingzi151/SummPip
공식 코드SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
https://github.com/morningmoni/RL-MMR
공식 코드Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
https://github.com/multilexsum/dataset
공식 코드Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
https://github.com/nlpyang/hiersumm
공식 코드https://github.com/oaimli/hgsum
공식 코드Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization
https://github.com/oaimli/peersum
공식 코드PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization
https://github.com/odelliab/HowSumm
공식 코드HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
https://github.com/oriern/SPARK
공식 코드https://github.com/oriern/SuperPAL
공식 코드Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
https://github.com/oriern/clusterprop
공식 코드Proposition-Level Clustering for Multi-Document Summarization
https://github.com/oriern/procluster
공식 코드Proposition-Level Clustering for Multi-Document Summarization
https://github.com/oswald1997/glimmer
공식 코드GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization
https://github.com/priberam/cera-summ
공식 코드Supervising the Centroid Baseline for Extractive Multi-Document Summarization
https://github.com/rajdeep345/ControllableSumm
공식 코드Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
https://github.com/ramakanth-pasunuru/QmdsCnnIr
공식 코드Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
https://github.com/ratishsp/centrum
공식 코드Multi-Document Summarization with Centroid-Based Pretraining
https://github.com/salesforce/diversesumm
공식 코드https://github.com/sebastianGehrmann/bottom-up-summary
공식 코드https://github.com/stevenlau6/bigsurvey
공식 코드Generating a Structured Summary of Numerous Academic Papers: Dataset and Method
https://github.com/tahmedge/WSL-DS-COLING-2020
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/ucfnlp/endorser-summ
공식 코드Modeling Endorsement for Multi-Document Abstractive Summarization
https://github.com/ucfnlp/multidoc_summarization
공식 코드Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.