Comments (2)
・Hellinger Distanceを用いてSentence Graphを構築.ラベル伝搬により要約に含める文を決定する手法
・update summarizationの研究ではsimilarityをはかるときにcosine similarityを用いることが多い.
・cosine similarityはユークリッド距離から直接的に導くことができる.
・Vector Space Modelはnonnegativeなmatrixを扱うので,確率的なアプローチで取り扱いたいが,ユークリッド距離は確率を扱うときにあまり良いmetricではない.そこでsqrt-cos similarityを提案する.sqrt-cosは,Hellinger Distanceから求めることができ,Hellinger Distanceは対称的で三角不等式を満たすなど,IRにおいて良いdistance measureの性質を持っている.(Hellinger Distanceを活用するために結果的に類似度の尺度としてsqrt-cosが出てきたとみなせる)
・またHellinger DistanceはKL Divergenceのsymmetric middle pointとみなすことができ,文書ベクトル生成においてはtf_idfとbinary weightingのちょうど中間のような重み付けを与えているとみなせる.
・要約を生成する際は,まずはset Aの文書群に対してMMR #243 を適用する(redundancyの項がmaxではなくて平均になっている).similarityはsqrt-cosを用いる.
・sqrt-cosと,set Aの要約結果を用いると,sentence graphを構築できる.sentence graphはset Aとset Bの各sentenceをノードとするグラフで,エッジの重みはsqrt-cosとなっている.このsentence graph上でset Aの要約結果のラベルをset B側のノードに伝搬させることで,要約に含めるべき文を選択する.
・ラベル伝搬にはGreen’s functionを用いる.set Bにlabel “1”がふられるものは,given topicとset Aのcontentsにrelevantなsentenceとなる.
・TAC2011のデータで評価した結果,standardなMMRを大幅にoutperform, co-ranking, Centroidベースの手法などよりも良い結果.
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うーん・・・
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