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41
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
Abstract: Many applications require generation of summaries tailored to the user’s information needs, i.e., their intent. Methods that express intent via explicit user queries fall short when query interpretation is subjective. Several datasets exist for summarization with objective intents where, for each document and intent (e.g., “weather”), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., “interesting places”) where different users will provide different summaries. We present SUBSUME, the first dataset for evaluation of SUBjective SUMmary Extraction systems. SUBSUME contains 2,200 (document, intent, summary) triplets over 48 Wikipedia pages, with ten intents of varying subjectivity, provided by 103 individuals over Mechanical Turk. We demonstrate statistically that the intents in SUBSUME vary systematically in subjectivity. To indicate SUBSUME’s usefulness, we explore a collection of baseline algorithms for subjective extractive summarization and show that (i) as ...
Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/39824-subsume-a-dataset-for-subjective-summary-extraction-from-wikipedia-documents
https://dx.doi.org/10.48448/rp8c-e676
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42
Few-Shot Named Entity Recognition: An Empirical Baseline Study ...
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43
Low-resource Taxonomy Enrichment with Pretrained Language Models ...
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44
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction ...
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45
Extend, donÕt rebuild: Phrasing conditional graph modification as autoregressive sequence labelling ...
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46
Treasures Outside Contexts: Improving Event Detection via Global Statistics ...
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47
Cost-effective End-to-end Information Extraction for Semi-structured Document Images ...
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48
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics. ...
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49
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data ...
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50
Robust Retrieval Augmented Generation for Zero-shot Slot Filling ...
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51
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation ...
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52
Structure-Augmented Keyphrase Generation ...
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53
Document-level Entity-based Extraction as Template Generation ...
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54
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network ...
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55
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction ...
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56
Data Augmentation for Cross-Domain Named Entity Recognition ...
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57
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction ...
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58
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection ...
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59
Active Learning by Acquiring Contrastive Examples ...
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60
A Bag of Tricks for Dialogue Summarization ...
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