Summarizing Entities: A Survey Report Shruti Chhabra ; and Srikanta Bedathur 徐丹云

Definition Resources Summarizing entities task Various documents Knowledge bases Summarizing entities task Extract information associated with entities from these sources Produce summaries

Categories Output Summary Type Number of Input Entities Single Entity Entity Pair Multiple Entities Textual Paragraph (biographical, definitional, domain-specific), Wiki-style Textual relations, Evidence trails Evidence trails Structural Property-value pair ranking, Structure extraction Relation extraction, Structure extraction

Textual-Single Entity Entity description “... Reuters reported that the radical Muslim group Hamas has claimed responsibility for the bombing act...“ noun phrases (NPs) Islamic Resistance Movement Hamas Paragraph summaries Wiki-style summaries Wikipedia

Textual-Single Entity-paragraph Generic Summarizaiton Biographical Summarization “Who is X” Use a top-down approach to search for patterns specific for biographies Utilize bottom up approach to further filter sentences which are biographical in nature Definitional queries “Who is X” and “What is X” Domain-specific Financial(company-specific entities) and geo-locational Distinctive characteristics

Textual-Single Entity-paragraph Generic Summarizaiton Topic signature[57], Conroy et al.[27] Graph based LexRank[34] Topic-sensitive version of LexRank, Otterbacher et al.[62]

Textual-Single Entity-paragraph net centroid value of the sentence position of the sentence in the document similarity with the first sentence LexRank QueryPhraseMatch sentence length tf.idf score similarity with the headline of the document Weighted frequency of entity alias or mention as pronoun Frequency of cue words associated with facets of a person frequency of words usually found with query entity

Textual-Single Entity-paragraph

Textual-Single Entity-Wiki Style distinctly presenting the various aspects of entity written in well structured manner Techniques learning templates Select content to fill these templates(Sauper and Barzilay [67], Autopedia system[92]) Selecting facts similar to infoboxes(Liu et al. [58])

Textual-Entity Pair find direct and indirect relations between entity pair Textual Relations(direct) Patty, Open IE the entities with corresponding relational pattern providing contextual evidence for the relationship between entities. Evidence Trails(indirect) sequence of sentences

Textual-Multiple Entity a connected graph for any number of inputs(Srihari et al. [70]) a concept chain graph (CCG) was built by processing a corpus graph matching technique ------ finding minimum steiner tree in CCG.

Structural-Single Entity Property-Value pair Ranking(1-hop) Structure Extraction System technique Frequency informativeness diversity popularity Cheng et al.[23] Random walk  Thalhammer et al. [81] Sydow et al. [78] Greedy algrithom Sydow et al. [75]

Structural-Entity Pair Relation Extraction facts relating given entities rule based, feature based and kernel based methods Structure Extraction paths or subgraphs

Structural-Entity Pair-Structure Extraction System Main idea Technique Graph Theory Faloutsos et al. [36] a connected subgraph, edge-weighted undirected graph, model the graph as a circuit maximizing the goodness function greedy heuristic Relational Databases (keyword search) Agrawal et al.[1] Symbol table: analogous to inverted lists in IR BANKS[12] Current single source shortest path algorithms from each source; Find root nodes connecting all the source nodes Knowledge Discovery Halaschek et al. [44] Paths between two entities rank list of relationship paths Anyanwu and Sheth [8] a path from each of these entities which meet at a common node a m-length path from entity such that the parent class of each node in one path is same as the parent class of corresponding node in other path

Structural-Multiple Entities mine an important subgraph for query nodes Tong and Faloutsos [82] retrieved nodes in a graph closely connected to query node and thus mined an important subgraph for query nodes. Kasneci et al. [52] Obtain steiner tree from the subgraph using STAR [53] algorithm

Comparison Textual Summaries Structural Summaries source Textual - documents Structural – knowledge bases information Rich in context poor cleanliness noisy Noise-free processable difficultly easily understandable Leverage the advantages of both text and structure Use knowledge bases to obtain connections among entities forming structural summaries augment with text to form textual summaries

Thanks