diff --git a/ch06-lsa/pom.xml b/ch06-lsa/pom.xml
index 5b43fea9..9add4b4b 100644
--- a/ch06-lsa/pom.xml
+++ b/ch06-lsa/pom.xml
@@ -38,6 +38,10 @@
       <groupId>edu.umd</groupId>
       <artifactId>cloud9</artifactId>
     </dependency>
+    <dependency>
+      <groupId>info.bliki.wiki</groupId>
+      <artifactId>bliki-core</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-client</artifactId>
diff --git a/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/AssembleDocumentTermMatrix.scala b/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/AssembleDocumentTermMatrix.scala
index 169e92bb..6bd6abe3 100644
--- a/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/AssembleDocumentTermMatrix.scala
+++ b/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/AssembleDocumentTermMatrix.scala
@@ -103,11 +103,11 @@ class AssembleDocumentTermMatrix(private val spark: SparkSession) extends Serial
    * Returns a document-term matrix where each element is the TF-IDF of the row's document and
    * the column's term.
    *
-   * @param docs a DF with two columns: title and text
+   * @param docTexts a DF with two columns: title and text
    */
-  def documentTermMatrix(docs: Dataset[(String, String)], stopWordsFile: String, numTerms: Int)
+  def documentTermMatrix(docTexts: Dataset[(String, String)], stopWordsFile: String, numTerms: Int)
     : (DataFrame, Array[String], Map[Long, String], Array[Double]) = {
-    val terms = contentsToTerms(docs, stopWordsFile)
+    val terms = contentsToTerms(docTexts, stopWordsFile)
 
     val termsDF = terms.toDF("title", "terms")
     val filtered = termsDF.where(size($"terms") > 1)
@@ -121,8 +121,7 @@ class AssembleDocumentTermMatrix(private val spark: SparkSession) extends Serial
 
     docTermFreqs.cache()
 
-    val docIdsDF = docTermFreqs.withColumn("id", monotonically_increasing_id)
-    val docIds = docIdsDF.select("id", "title").as[(Long, String)].collect().toMap
+    val docIds = docTermFreqs.rdd.map(_.getString(0)).zipWithUniqueId().map(_.swap).collect().toMap
 
     val idf = new IDF().setInputCol("termFreqs").setOutputCol("tfidfVec")
     val idfModel = idf.fit(docTermFreqs)
diff --git a/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/RunLSA.scala b/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/RunLSA.scala
index b00a2425..5509991c 100644
--- a/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/RunLSA.scala
+++ b/ch06-lsa/src/main/scala/com/cloudera/datascience/lsa/RunLSA.scala
@@ -20,7 +20,7 @@ import scala.collection.mutable.ArrayBuffer
 object RunLSA {
   def main(args: Array[String]): Unit = {
     val k = if (args.length > 0) args(0).toInt else 100
-    val numTerms = if (args.length > 1) args(1).toInt else 50000
+    val numTerms = if (args.length > 1) args(1).toInt else 20000
 
     val spark = SparkSession.builder().config("spark.serializer", classOf[KryoSerializer].getName).getOrCreate()
     val assembleMatrix = new AssembleDocumentTermMatrix(spark)