Papers by Abram Handler
Relational Summarization for Corpus Analysis (N18-1)
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| Challenge: | Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied. |
| Approach: | They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
| Outcome: | The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
Query-focused Sentence Compression in Linear Time (D19-1)
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| Challenge: | Existing techniques for constrained compression are slow and require third-party solvers. |
| Approach: | They propose a query-focused sentence compression technique which constructs length and lexically constrained compressions in linear time by growing a subgraph in the dependency parse of a sentence. |
| Outcome: | The proposed technique achieves an 11x empirical speedup over baseline methods while improving query-focused applications. |
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts (D19-1)
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| Challenge: | a recent study shows that sports broadcasters build drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. |
| Approach: | They use FOOTBALL to examine racial bias in sports commentary . they identify major confounding factors for researchers examining rraecial bias . |
| Outcome: | The proposed dataset supports previous social science studies on commentator bias . it contains 1,455 broadcast football transcripts annotated with 250K player mentions and racial metadata . |
Summarizing Relationships for Interactive Concept Map Browsers (D19-54)
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| Challenge: | Concept maps are visual summaries, structured as directed graphs . initial attempts to generate static summary models focused on static summarization . however, in interactive settings, users will need to dynamically query relationships . |
| Approach: | They propose a model which returns a labeled summary of a query concept for display in a visual interface. |
| Outcome: | The proposed model can summarize relationships between two query concepts in a visual network . it is based on a new dataset, and is trained on the dataset . |